<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>The Tagaroo Blog</title><description>Field notes on annotation: inter-rater reliability, clinical rating scales, qualitative coding, image labeling, and running annotation campaigns that hold up.</description><link>https://tagaroo.ai/blog/</link><item><title>VR-CoDES Coding System: Cues, Concerns, and Responses</title><link>https://tagaroo.ai/blog/vr-codes-emotional-cues-concerns/</link><guid isPermaLink="true">https://tagaroo.ai/blog/vr-codes-emotional-cues-concerns/</guid><description>How the VR-CoDES coding system codes patient emotional cues versus concerns and the provider response, using the providing-space vs reducing-space axis.</description><pubDate>Wed, 22 Jul 2026 13:00:00 GMT</pubDate><category>healthcare communication</category><category>emotion coding</category><category>consultation</category><category>coding scheme</category><category>annotation</category></item><item><title>Annotation Fatigue: Why Label Quality Drops Over Time</title><link>https://tagaroo.ai/blog/annotation-fatigue-vigilance-decrement/</link><guid isPermaLink="true">https://tagaroo.ai/blog/annotation-fatigue-vigilance-decrement/</guid><description>Annotation fatigue erodes label quality within a session. See the vigilance-decrement evidence and the breaks, caps, and catch-trials that hold it steady.</description><pubDate>Wed, 22 Jul 2026 00:00:00 GMT</pubDate><category>annotator-wellbeing</category><category>data-quality</category><category>vigilance</category><category>annotation-workflow</category><category>inter-rater-reliability</category></item><item><title>Annotator Burnout: Causes, Warning Signs, and Prevention</title><link>https://tagaroo.ai/blog/annotator-burnout-prevention/</link><guid isPermaLink="true">https://tagaroo.ai/blog/annotator-burnout-prevention/</guid><description>Annotator burnout has clear causes and warning signs. Learn the drivers, symptoms to watch for, and the workload and rotation practices that prevent it.</description><pubDate>Wed, 22 Jul 2026 00:00:00 GMT</pubDate><category>annotator-wellbeing</category><category>burnout</category><category>content-moderation</category><category>annotation-workload</category><category>responsible-ai</category></item><item><title>Confidence Interval for Kappa, Alpha, and ICC, Done Right</title><link>https://tagaroo.ai/blog/confidence-intervals-for-agreement/</link><guid isPermaLink="true">https://tagaroo.ai/blog/confidence-intervals-for-agreement/</guid><description>Why a bare agreement number misleads, and how to build a confidence interval for kappa, alpha, and the ICC. See analytic vs bootstrap methods.</description><pubDate>Wed, 22 Jul 2026 00:00:00 GMT</pubDate><category>reliability</category><category>statistics</category><category>inter-rater-reliability</category></item><item><title>Evidence Mode vs Instance Mode: Evidence-Based Annotation</title><link>https://tagaroo.ai/blog/evidence-mode-vs-instance-mode-annotation/</link><guid isPermaLink="true">https://tagaroo.ai/blog/evidence-mode-vs-instance-mode-annotation/</guid><description>Evidence mode rates the whole transcript once and cites its supporting spans; instance mode tags each occurrence. How the choice reshapes reliability math.</description><pubDate>Wed, 22 Jul 2026 00:00:00 GMT</pubDate><category>annotation methodology</category><category>reliability</category><category>coding schemes</category><category>machine learning</category></item><item><title>Ethical Data Annotation: A Practical Fair-Work Standard</title><link>https://tagaroo.ai/blog/fair-work-data-annotation/</link><guid isPermaLink="true">https://tagaroo.ai/blog/fair-work-data-annotation/</guid><description>Ethical data annotation means fair pay, conditions, contracts, management, and representation. Audit any vendor against the Fairwork standard.</description><pubDate>Wed, 22 Jul 2026 00:00:00 GMT</pubDate><category>data-labeling</category><category>ai-ethics</category><category>responsible-ai</category><category>fair-work</category><category>vendor-due-diligence</category></item><item><title>Sensitive Content Annotation: A Humane, Practical Protocol</title><link>https://tagaroo.ai/blog/humane-sensitive-content-annotation/</link><guid isPermaLink="true">https://tagaroo.ai/blog/humane-sensitive-content-annotation/</guid><description>Concrete protections for sensitive content annotation: informed consent, exposure limits, grayscale and blur defaults, and debriefing. Get the protocol.</description><pubDate>Wed, 22 Jul 2026 00:00:00 GMT</pubDate><category>annotator-wellbeing</category><category>content-moderation</category><category>trauma-informed</category><category>responsible-ai</category><category>annotation-ux</category></item><item><title>Localize a Rating Scale: Annotating in a Second Language</title><link>https://tagaroo.ai/blog/localizing-rating-scales-annotation/</link><guid isPermaLink="true">https://tagaroo.ai/blog/localizing-rating-scales-annotation/</guid><description>Localize a rating scale for coding in a second language: what back-translation validates, what it misses, and how to re-anchor the coding examples.</description><pubDate>Wed, 22 Jul 2026 00:00:00 GMT</pubDate><category>rating scales</category><category>translation</category><category>cross-cultural</category><category>methodology</category></item><item><title>Multi-Layer Annotation: Stacking Scales on One Transcript</title><link>https://tagaroo.ai/blog/multi-layer-scale-annotation/</link><guid isPermaLink="true">https://tagaroo.ai/blog/multi-layer-scale-annotation/</guid><description>Multi-layer annotation stacks several coding schemes and rating scales on one transcript. Keep layers independent, handle overlaps, score each separately.</description><pubDate>Wed, 22 Jul 2026 00:00:00 GMT</pubDate><category>annotation-methodology</category><category>multi-layer-annotation</category><category>coding-schemes</category><category>inter-rater-reliability</category></item><item><title>Percent Agreement, Reconsidered: When It&apos;s Actually Fine</title><link>https://tagaroo.ai/blog/percent-agreement-reconsidered/</link><guid isPermaLink="true">https://tagaroo.ai/blog/percent-agreement-reconsidered/</guid><description>Raw percent agreement isn&apos;t useless. When to chance-correct, when kappa misleads, and when agreement plus a confidence interval is the honest report.</description><pubDate>Wed, 22 Jul 2026 00:00:00 GMT</pubDate><category>reliability</category><category>statistics</category><category>inter-rater-reliability</category></item><item><title>Rating Scale vs Coding Scheme: Which Does Your Study Need?</title><link>https://tagaroo.ai/blog/rating-scales-vs-coding-schemes/</link><guid isPermaLink="true">https://tagaroo.ai/blog/rating-scales-vs-coding-schemes/</guid><description>Rating scale vs coding scheme: how dimensional severity and categorical coding change your unit of analysis, reliability statistic, and annotation UI.</description><pubDate>Wed, 22 Jul 2026 00:00:00 GMT</pubDate><category>annotation-methodology</category><category>rating-scales</category><category>coding-schemes</category><category>inter-rater-reliability</category></item><item><title>Annotation Consensus Methods: Majority Vote to Dawid-Skene</title><link>https://tagaroo.ai/blog/adjudication-and-consensus-methods/</link><guid isPermaLink="true">https://tagaroo.ai/blog/adjudication-and-consensus-methods/</guid><description>How to turn many annotator labels into one gold label: majority vote, Dawid-Skene, MACE, STAPLE, and expert adjudication, with a table for when each fits.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>annotation-operations</category><category>consensus</category><category>aggregation</category><category>statistics</category></item><item><title>Span-Level Agreement: Why Kappa Fails, Use F1 and IoU</title><link>https://tagaroo.ai/blog/agreement-for-spans-f1-iou/</link><guid isPermaLink="true">https://tagaroo.ai/blog/agreement-for-spans-f1-iou/</guid><description>Cohen&apos;s kappa needs a fixed item set that span and NER annotation never has. Measure span-level agreement with pairwise F1 and IoU instead—see how.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>reliability</category><category>annotation</category><category>nlp</category><category>statistics</category></item><item><title>RLHF Data Annotation: A Playbook for Preference &amp; Eval</title><link>https://tagaroo.ai/blog/annotating-for-rlhf-llm-eval/</link><guid isPermaLink="true">https://tagaroo.ai/blog/annotating-for-rlhf-llm-eval/</guid><description>An RLHF data annotation playbook: pairwise preference vs Likert rubrics, reward-model data, eval sets, and rater agreement. See the decision guide.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>rlhf</category><category>llm-evaluation</category><category>preference-data</category><category>inter-rater-reliability</category><category>data-annotation</category></item><item><title>Text Annotation Use Cases: Support, Legal &amp; Survey Text</title><link>https://tagaroo.ai/blog/annotation-beyond-clinical/</link><guid isPermaLink="true">https://tagaroo.ai/blog/annotation-beyond-clinical/</guid><description>How teams apply text annotation use cases beyond the clinic: support-ticket intent, legal clause tagging, and survey coding, with span evidence and IRR.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>text-annotation</category><category>coding-schemes</category><category>inter-rater-reliability</category><category>nlp</category></item><item><title>Annotation Cost Calculator: Budget and Timeline Math</title><link>https://tagaroo.ai/blog/annotation-budget-calculator/</link><guid isPermaLink="true">https://tagaroo.ai/blog/annotation-budget-calculator/</guid><description>An annotation cost calculator: turn items, labels per item, time, wage, redundancy, and QA overhead into a labeling budget and timeline. See the math.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>annotation-budget</category><category>cost-calculator</category><category>project-management</category><category>methods</category></item><item><title>Data Annotation Consent &amp; Licensing: HIPAA, GDPR &amp; IP</title><link>https://tagaroo.ai/blog/annotation-data-consent-licensing/</link><guid isPermaLink="true">https://tagaroo.ai/blog/annotation-data-consent-licensing/</guid><description>Data annotation consent explained: HIPAA de-identification vs a BAA, GDPR special-category data, who owns the labels, and how to license a dataset.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>compliance</category><category>HIPAA</category><category>GDPR</category><category>data-licensing</category><category>data-privacy</category></item><item><title>Ground Truth for Medical AI: Reference Standards &amp; the FDA</title><link>https://tagaroo.ai/blog/annotation-for-fda-medical-ai/</link><guid isPermaLink="true">https://tagaroo.ai/blog/annotation-for-fda-medical-ai/</guid><description>How ground truth for medical AI is built for FDA review: reference standards, expert panels, MRMC reader studies, and the SegAgree agreement idea.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>image-annotation</category><category>medical-imaging</category><category>regulatory</category><category>ground-truth</category></item><item><title>How to Write Annotation Guidelines That Reduce Disagreement</title><link>https://tagaroo.ai/blog/annotation-guidelines-that-work/</link><guid isPermaLink="true">https://tagaroo.ai/blog/annotation-guidelines-that-work/</guid><description>Write annotation guidelines that reduce annotator disagreement: operational definitions, positive and negative examples, and an edge-case decision log.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>annotation-guidelines</category><category>coding-manual</category><category>methods</category><category>inter-rater-reliability</category></item><item><title>Audio Annotation: From Transcription to Labeled Events</title><link>https://tagaroo.ai/blog/audio-speech-annotation-guide/</link><guid isPermaLink="true">https://tagaroo.ai/blog/audio-speech-annotation-guide/</guid><description>Audio annotation turns raw speech into labeled data: transcription, speaker diarization, timestamped events, and prosody tags. Compare the types.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>audio-annotation</category><category>speech</category><category>diarization</category><category>guide</category></item><item><title>Auditable Clinical Ratings: Score From the Transcript</title><link>https://tagaroo.ai/blog/auditable-scale-scoring/</link><guid isPermaLink="true">https://tagaroo.ai/blog/auditable-scale-scoring/</guid><description>Build an auditable clinical rating by tying every scale item to a quoted line, not a global impression. See how span-grounded scoring works.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>rating scales</category><category>clinical trials</category><category>reliability</category><category>regulatory</category></item><item><title>BPRS vs PANSS: The Two Standard Psychosis Rating Scales</title><link>https://tagaroo.ai/blog/bprs-vs-panss/</link><guid isPermaLink="true">https://tagaroo.ai/blog/bprs-vs-panss/</guid><description>BPRS vs PANSS compared: item overlap, the positive-negative-general structure, trial usage, and score linking. See which psychosis scale to use.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>psychosis</category><category>rating scales</category><category>schizophrenia</category></item><item><title>CBCA vs Reality Monitoring: Two Credibility Methods</title><link>https://tagaroo.ai/blog/cbca-vs-reality-monitoring/</link><guid isPermaLink="true">https://tagaroo.ai/blog/cbca-vs-reality-monitoring/</guid><description>CBCA vs reality monitoring: how two statement-credibility methods compare on criteria, accuracy, and court limits—and why neither one detects lies.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>forensic</category><category>credibility assessment</category><category>statement analysis</category><category>deception detection</category><category>annotation</category></item><item><title>Chest X-ray Annotation: A Worked Example for AI Datasets</title><link>https://tagaroo.ai/blog/chest-xray-annotation-example/</link><guid isPermaLink="true">https://tagaroo.ai/blog/chest-xray-annotation-example/</guid><description>A finding-by-finding chest X-ray annotation example: build a CheXpert-style schema, handle uncertainty labels, and pick image-level vs region labels.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>image-annotation</category><category>medical-imaging</category><category>chest-xray</category><category>data-labeling</category></item><item><title>Which ICC to Use: Choosing Model, Type, and Definition</title><link>https://tagaroo.ai/blog/choosing-an-icc/</link><guid isPermaLink="true">https://tagaroo.ai/blog/choosing-an-icc/</guid><description>Which ICC to use for continuous ratings: choose the model (1, 2, or 3), single vs average measures, and absolute agreement vs consistency, from one table.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>reliability</category><category>statistics</category><category>inter-rater-reliability</category></item><item><title>Clinician-Rated vs Self-Report Scales: A Methods Trade-off</title><link>https://tagaroo.ai/blog/clinician-rated-vs-self-report-scales/</link><guid isPermaLink="true">https://tagaroo.ai/blog/clinician-rated-vs-self-report-scales/</guid><description>Clinician-rated vs self-report scales compared: who rates, the bias each carries, reliability and cost trade-offs, and transcript coding as a third path.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>rating scales</category><category>methodology</category><category>clinical trials</category></item><item><title>Criteria-Based Content Analysis: The 19 CBCA Criteria</title><link>https://tagaroo.ai/blog/criteria-based-content-analysis-cbca/</link><guid isPermaLink="true">https://tagaroo.ai/blog/criteria-based-content-analysis-cbca/</guid><description>Criteria-based content analysis (CBCA) rates 19 content criteria to judge whether a statement reads as experience-based—an aid, not a lie test. See all 19.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>forensic</category><category>credibility assessment</category><category>statement analysis</category><category>investigative interviewing</category><category>annotation</category></item><item><title>Data Cascades: Why Fixing Labels Beats Tuning Models</title><link>https://tagaroo.ai/blog/data-cascades-data-centric-ai/</link><guid isPermaLink="true">https://tagaroo.ai/blog/data-cascades-data-centric-ai/</guid><description>Data cascades are compounding downstream failures from upstream data problems. 92% of AI teams hit them—here&apos;s why fixing labels beats tuning the model.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>annotation-quality</category><category>data-centric-ai</category><category>data-cascades</category><category>label-quality</category></item><item><title>Depression Rating Scales Compared: HAM-D, MADRS, PHQ-9</title><link>https://tagaroo.ai/blog/depression-rating-scales-compared/</link><guid isPermaLink="true">https://tagaroo.ai/blog/depression-rating-scales-compared/</guid><description>Depression rating scales compared: HAM-D, MADRS, PHQ-9, and content analysis by rater, item count, and change-sensitivity. See which fits your study.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>depression</category><category>rating scales</category><category>clinical trials</category></item><item><title>Dice vs IoU vs Hausdorff: Segmentation Metrics Explained</title><link>https://tagaroo.ai/blog/dice-vs-iou-vs-hausdorff/</link><guid isPermaLink="true">https://tagaroo.ai/blog/dice-vs-iou-vs-hausdorff/</guid><description>Dice vs IoU, the Jaccard index, and Hausdorff distance compared: formulas, the Dice-IoU identity, and when overlap metrics mislead. See which to report.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>image-annotation</category><category>segmentation</category><category>metrics</category><category>statistics</category></item><item><title>DICOM Annotation: De-Identify and Prepare Data for AI</title><link>https://tagaroo.ai/blog/dicom-annotation-data-prep/</link><guid isPermaLink="true">https://tagaroo.ai/blog/dicom-annotation-data-prep/</guid><description>A DICOM annotation and data-prep guide: strip header and burned-in pixel PHI, window images for display, and store labels as DICOM-SEG, SR, or JSON.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>image-annotation</category><category>medical-imaging</category><category>dicom</category><category>de-identification</category></item><item><title>Annotator Disagreement Is Signal, Not Noise to Discard</title><link>https://tagaroo.ai/blog/disagreement-is-signal-not-noise/</link><guid isPermaLink="true">https://tagaroo.ai/blog/disagreement-is-signal-not-noise/</guid><description>Forcing one gold label can throw away real information. See why annotator disagreement is signal, not noise, and when to reconcile versus preserve it.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>annotation-quality</category><category>inter-rater-reliability</category><category>human-label-variation</category><category>data-centric-ai</category></item><item><title>Document Annotation: Layout, Entities &amp; Key-Value Fields</title><link>https://tagaroo.ai/blog/document-pdf-annotation/</link><guid isPermaLink="true">https://tagaroo.ai/blog/document-pdf-annotation/</guid><description>How document annotation works for ML: layout regions, document entities, key-value pairs, and table structure, plus the datasets. See the tasks.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>document-annotation</category><category>information-extraction</category><category>layout</category><category>ocr</category></item><item><title>Does Pay Improve Annotation Quality? What Research Shows</title><link>https://tagaroo.ai/blog/does-pay-improve-annotation-quality/</link><guid isPermaLink="true">https://tagaroo.ai/blog/does-pay-improve-annotation-quality/</guid><description>Does pay improve annotation quality? Higher pay lifts speed, participation, and fairness—but rarely accuracy. See what really drives label quality.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>annotator-quality</category><category>crowdsourcing</category><category>data-labeling</category><category>annotation-workflow</category></item><item><title>DSM Criteria Annotation: From Diagnosis to Taggable Spans</title><link>https://tagaroo.ai/blog/dsm-criteria-to-annotation/</link><guid isPermaLink="true">https://tagaroo.ai/blog/dsm-criteria-to-annotation/</guid><description>Turn DSM-5 and ICD-11 criteria into observable, taggable phenomena: a worked MDD-to-PHQ-9 mapping and why you annotate symptoms, not a diagnosis.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>diagnostic criteria</category><category>annotation</category><category>methods</category><category>operationalization</category></item><item><title>Expert vs Crowd Annotation: When Expertise Really Matters</title><link>https://tagaroo.ai/blog/expert-vs-crowd-annotation/</link><guid isPermaLink="true">https://tagaroo.ai/blog/expert-vs-crowd-annotation/</guid><description>Expert vs crowd annotation: aggregated non-experts match experts on many tasks, but clinical judgment breaks the ratio. Get the decision guide.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>annotator-quality</category><category>crowdsourcing</category><category>data-labeling</category><category>annotation-workflow</category></item><item><title>How to Find Label Errors in Dataset Quality Audits</title><link>https://tagaroo.ai/blog/find-label-errors-in-your-dataset/</link><guid isPermaLink="true">https://tagaroo.ai/blog/find-label-errors-in-your-dataset/</guid><description>Label errors hide in almost every dataset. Learn how to find label errors in dataset audits with disagreement, model confidence, and confident learning.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>annotation-quality</category><category>label-noise</category><category>data-centric-ai</category><category>confident-learning</category></item><item><title>GAD-7 vs HAM-A: Self-Report vs Clinician-Rated Anxiety</title><link>https://tagaroo.ai/blog/gad7-vs-hama/</link><guid isPermaLink="true">https://tagaroo.ai/blog/gad7-vs-hama/</guid><description>GAD-7 vs HAM-A compared: the 7-item self-report anxiety screen against the 14-item clinician-rated severity endpoint. See which anxiety scale to use.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>anxiety</category><category>rating scales</category><category>clinical trials</category></item><item><title>Geospatial Image Annotation: Labeling Beyond the Everyday</title><link>https://tagaroo.ai/blog/geospatial-scientific-image-annotation/</link><guid isPermaLink="true">https://tagaroo.ai/blog/geospatial-scientific-image-annotation/</guid><description>Geospatial image annotation covers satellite, aerial, and microscopy imagery—where huge rasters, rare classes, and expert-only labels change the rules.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>image-annotation</category><category>remote-sensing</category><category>scientific-imaging</category><category>inter-rater-reliability</category></item><item><title>Gold Questions &amp; Honeypots: Annotation QA That Works</title><link>https://tagaroo.ai/blog/gold-questions-honeypots-annotation-qa/</link><guid isPermaLink="true">https://tagaroo.ai/blog/gold-questions-honeypots-annotation-qa/</guid><description>Gold questions, honeypots, and attention checks catch bad annotations—if thresholds spare legitimate disagreement. See how each QA method works.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>annotation-quality</category><category>quality-control</category><category>crowdsourcing</category><category>attention-checks</category></item><item><title>Gwet&apos;s AC1 and AC2: A Fix for Kappa&apos;s Prevalence Paradox</title><link>https://tagaroo.ai/blog/gwet-ac1-kappa-paradox/</link><guid isPermaLink="true">https://tagaroo.ai/blog/gwet-ac1-kappa-paradox/</guid><description>Why Cohen&apos;s kappa collapses under skewed prevalence, and how Gwet&apos;s AC1 and AC2 chance-correction fixes it—with the formula and a worked example.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>reliability</category><category>statistics</category><category>inter-rater-reliability</category></item><item><title>How Many Annotators Medical Images Need: A Consensus Guide</title><link>https://tagaroo.ai/blog/how-many-annotators-medical-images/</link><guid isPermaLink="true">https://tagaroo.ai/blog/how-many-annotators-medical-images/</guid><description>How many annotators medical images need depends on clinical risk, from spot-checking low-risk labels to blind multi-reader reads with adjudication.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>image-annotation</category><category>medical-imaging</category><category>inter-rater-reliability</category><category>annotation-workflow</category></item><item><title>How Many Annotators Per Item? Redundancy and the Cost Curve</title><link>https://tagaroo.ai/blog/how-many-annotators-per-item/</link><guid isPermaLink="true">https://tagaroo.ai/blog/how-many-annotators-per-item/</guid><description>How many annotators per item do you need? One label is risky. Compare majority vote, Dawid-Skene, and MACE, and see where extra labels stop paying off.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>annotator-quality</category><category>crowdsourcing</category><category>data-labeling</category><category>label-aggregation</category><category>inter-rater-reliability</category></item><item><title>The Human Cost of Data Labeling: Ghost Work Behind AI</title><link>https://tagaroo.ai/blog/human-cost-of-data-labeling/</link><guid isPermaLink="true">https://tagaroo.ai/blog/human-cost-of-data-labeling/</guid><description>The human cost of data labeling: the underpaid, invisible ghost workers behind AI, the toll of the work, and what responsible sourcing looks like.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>data-labeling</category><category>ai-ethics</category><category>ghost-work</category><category>content-moderation</category><category>responsible-ai</category></item><item><title>Who Should Annotate Your Data? A Selection Framework</title><link>https://tagaroo.ai/blog/ideal-annotator-background/</link><guid isPermaLink="true">https://tagaroo.ai/blog/ideal-annotator-background/</guid><description>Who should annotate your data? Match annotator background to the task—expert, trained non-expert, or crowd—using a clear selection framework and matrix.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>annotator-quality</category><category>annotation-workflow</category><category>crowdsourcing</category><category>data-labeling</category></item><item><title>Image Annotation Types: Cost vs Precision, Explained</title><link>https://tagaroo.ai/blog/image-annotation-types-explained/</link><guid isPermaLink="true">https://tagaroo.ai/blog/image-annotation-types-explained/</guid><description>Bounding box, polygon, semantic vs instance vs panoptic masks, or keypoints? Compare image annotation types by cost, precision, and downstream task.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>image-annotation</category><category>computer-vision</category><category>segmentation</category><category>comparison</category></item><item><title>Image Labeling Guidelines Annotators Can Actually Apply</title><link>https://tagaroo.ai/blog/image-labeling-taxonomy-design/</link><guid isPermaLink="true">https://tagaroo.ai/blog/image-labeling-taxonomy-design/</guid><description>Write image labeling guidelines annotators actually apply: mutually exclusive vs overlapping classes, visual examples, and clear when-in-doubt rules.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>image-annotation</category><category>taxonomy-design</category><category>annotation-guidelines</category><category>computer-vision</category></item><item><title>Image Segmentation Agreement: Dice, IoU, and STAPLE</title><link>https://tagaroo.ai/blog/image-segmentation-agreement-dice-iou-staple/</link><guid isPermaLink="true">https://tagaroo.ai/blog/image-segmentation-agreement-dice-iou-staple/</guid><description>Why Cohen&apos;s kappa fails on pixels, and how Dice, IoU, and the STAPLE algorithm measure image segmentation agreement across annotators. See the math.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>image-annotation</category><category>segmentation</category><category>agreement</category><category>statistics</category></item><item><title>IRF Classroom Discourse: Coding Teacher-Student Talk</title><link>https://tagaroo.ai/blog/irf-ire-classroom-discourse/</link><guid isPermaLink="true">https://tagaroo.ai/blog/irf-ire-classroom-discourse/</guid><description>IRF classroom discourse is the three-move Initiation-Response-Feedback exchange behind most teacher talk. Learn to code it and read the F move.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>education</category><category>classroom discourse</category><category>interaction analysis</category><category>pedagogy</category><category>annotation</category></item><item><title>Krippendorff&apos;s Alpha: Nominal, Ordinal &amp; Missing Data</title><link>https://tagaroo.ai/blog/krippendorffs-alpha-computed/</link><guid isPermaLink="true">https://tagaroo.ai/blog/krippendorffs-alpha-computed/</guid><description>How to compute Krippendorff&apos;s alpha: the observed-over-expected disagreement formula for nominal, ordinal, and missing data, with a worked example.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>reliability</category><category>statistics</category><category>krippendorff-alpha</category><category>content-analysis</category></item><item><title>Medical Image Annotation: A Complete Guide for AI Teams</title><link>https://tagaroo.ai/blog/medical-image-annotation-guide/</link><guid isPermaLink="true">https://tagaroo.ai/blog/medical-image-annotation-guide/</guid><description>Medical image annotation, end to end: annotation types, who should label, consensus and adjudication, DICOM de-identification, and QA tied to risk.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>image-annotation</category><category>medical-imaging</category><category>annotation-workflow</category><category>data-labeling</category></item><item><title>Change Talk vs Sustain Talk: Coding Client Language</title><link>https://tagaroo.ai/blog/misc-change-talk-sustain-talk/</link><guid isPermaLink="true">https://tagaroo.ai/blog/misc-change-talk-sustain-talk/</guid><description>Change talk vs sustain talk, coded: how the MISC classifies client language, why the balance predicts outcomes, and how to segment it reliably.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>motivational interviewing</category><category>change talk</category><category>therapy process</category><category>client language</category><category>annotation</category></item><item><title>MISC vs MITI: Coding the Client vs the Clinician in MI</title><link>https://tagaroo.ai/blog/misc-vs-miti/</link><guid isPermaLink="true">https://tagaroo.ai/blog/misc-vs-miti/</guid><description>MISC vs MITI, disambiguated: the MITI codes the clinician&apos;s MI fidelity; the MISC codes the client&apos;s change and sustain talk. Compare purpose and use.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>motivational interviewing</category><category>therapy process</category><category>fidelity coding</category><category>change talk</category><category>annotation</category></item><item><title>MITI Motivational Interviewing: A Fidelity Coding Guide</title><link>https://tagaroo.ai/blog/miti-motivational-interviewing-coding/</link><guid isPermaLink="true">https://tagaroo.ai/blog/miti-motivational-interviewing-coding/</guid><description>How MITI motivational interviewing coding works: behavior counts, four global scores, and the R:Q ratio and %complex reflections that grade MI fidelity.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>motivational interviewing</category><category>therapy process</category><category>fidelity coding</category><category>counseling</category><category>annotation</category></item><item><title>Model-Assisted Labeling for Images: Label Less, Cover More</title><link>https://tagaroo.ai/blog/model-assisted-image-labeling/</link><guid isPermaLink="true">https://tagaroo.ai/blog/model-assisted-image-labeling/</guid><description>Model-assisted labeling lets a model draft image labels so annotators correct, not create. How pre-labeling and active learning cut work honestly.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>image-annotation</category><category>active-learning</category><category>annotation-workflow</category><category>data-labeling</category></item><item><title>Multimodal Annotation for Foundation Models: One Schema</title><link>https://tagaroo.ai/blog/multimodal-annotation-foundation-models/</link><guid isPermaLink="true">https://tagaroo.ai/blog/multimodal-annotation-foundation-models/</guid><description>Annotate aligned text, image, and audio in one schema for foundation models: cross-modal alignment, unified taxonomies, and QA that keeps labels in sync.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>multimodal</category><category>foundation-models</category><category>vision-language</category><category>audio-annotation</category><category>data-labeling</category></item><item><title>Multimodal Annotation: Linking Images to Their Reports</title><link>https://tagaroo.ai/blog/multimodal-image-text-annotation/</link><guid isPermaLink="true">https://tagaroo.ai/blog/multimodal-image-text-annotation/</guid><description>How to build multimodal image-text annotation datasets: pair reports to images, ground report phrases to regions, and link findings across modalities.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>image-annotation</category><category>multimodal</category><category>vision-language</category><category>medical-imaging</category><category>data-labeling</category></item><item><title>Whole-Slide Image Annotation: A Guide for Pathology AI</title><link>https://tagaroo.ai/blog/pathology-wsi-annotation/</link><guid isPermaLink="true">https://tagaroo.ai/blog/pathology-wsi-annotation/</guid><description>Whole-slide image annotation for computational pathology: the gigapixel challenge, the slide-to-cell label hierarchy, and pathologist-in-the-loop QA.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>image-annotation</category><category>computational-pathology</category><category>digital-pathology</category><category>data-labeling</category></item><item><title>How to Run a Pilot Annotation Round Before You Scale</title><link>https://tagaroo.ai/blog/pilot-annotation-round/</link><guid isPermaLink="true">https://tagaroo.ai/blog/pilot-annotation-round/</guid><description>Run a pilot annotation round before you scale: scope, sample size, double-coding, measuring agreement, and revising the codebook. See the checklist.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>pilot-annotation</category><category>codebook</category><category>inter-rater-reliability</category><category>methods</category></item><item><title>Sample Size for Inter-Rater Reliability: Subjects and Raters</title><link>https://tagaroo.ai/blog/reliability-study-sample-size/</link><guid isPermaLink="true">https://tagaroo.ai/blog/reliability-study-sample-size/</guid><description>Set the sample size for inter-rater reliability: how many subjects and raters pin kappa or an ICC to a target CI width. See the planning tables.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>reliability</category><category>statistics</category><category>inter-rater-reliability</category></item><item><title>How to Report Inter-Rater Reliability: A Methods Template</title><link>https://tagaroo.ai/blog/reporting-irr-template/</link><guid isPermaLink="true">https://tagaroo.ai/blog/reporting-irr-template/</guid><description>A fill-in-the-blanks methods template for how to report inter-rater reliability: coefficient, raters, unit of analysis, CI, and software, per GRRAS.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>reliability</category><category>statistics</category><category>inter-rater-reliability</category></item><item><title>Rating Scale to Codebook: Operationalize Any Instrument</title><link>https://tagaroo.ai/blog/scale-to-codebook/</link><guid isPermaLink="true">https://tagaroo.ai/blog/scale-to-codebook/</guid><description>A step-by-step method to go from rating scale to codebook: turn each item into a code, anchors, examples, and decision rules coders apply consistently.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>codebook</category><category>methods</category><category>annotation</category><category>rating scales</category></item><item><title>Improve Annotator Quality: Screen and Train, Don&apos;t Filter</title><link>https://tagaroo.ai/blog/screening-training-annotators/</link><guid isPermaLink="true">https://tagaroo.ai/blog/screening-training-annotators/</guid><description>To improve annotator quality, screen candidates for aptitude and train them before coding—filtering bad labels later loses. See the evidence.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>annotator-quality</category><category>crowdsourcing</category><category>annotation-workflow</category><category>data-labeling</category></item><item><title>Synthetic Data vs Human Annotation: When to Use Which</title><link>https://tagaroo.ai/blog/synthetic-data-vs-human-annotation/</link><guid isPermaLink="true">https://tagaroo.ai/blog/synthetic-data-vs-human-annotation/</guid><description>Synthetic data vs human annotation: when LLM-generated labels are cheap and good, where they miss the tail, and how to build a hybrid you can trust.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>synthetic-data</category><category>annotation-workflow</category><category>weak-supervision</category><category>data-labeling</category></item><item><title>TLC vs SAPS Thought Disorder: Coding Disorganized Speech</title><link>https://tagaroo.ai/blog/tlc-vs-saps-thought-disorder/</link><guid isPermaLink="true">https://tagaroo.ai/blog/tlc-vs-saps-thought-disorder/</guid><description>TLC vs SAPS thought disorder, compared: the 18-item TLC glossary vs the 8-item SAPS positive-FTD subscale, with an overlap grid and when to use each.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>thought disorder</category><category>rating scales</category><category>schizophrenia</category><category>psychosis</category><category>speech</category></item><item><title>Which Inter-Rater Reliability Coefficient Should You Use?</title><link>https://tagaroo.ai/blog/which-irr-coefficient/</link><guid isPermaLink="true">https://tagaroo.ai/blog/which-irr-coefficient/</guid><description>Which inter-rater reliability coefficient to use, by data type, rater count, and missing data—a decision guide to kappa, alpha, AC1, and ICC.</description><pubDate>Tue, 21 Jul 2026 00:00:00 GMT</pubDate><category>reliability</category><category>statistics</category><category>inter-rater-reliability</category></item><item><title>RLHF Annotation Tools: Match the Task to the Right Tool</title><link>https://tagaroo.ai/blog/annotation-tools-for-llm-rlhf/</link><guid isPermaLink="true">https://tagaroo.ai/blog/annotation-tools-for-llm-rlhf/</guid><description>Compare RLHF annotation tools by task—SFT demos, preference ranking, red-teaming, eval rubrics—and find which tool fits each. See the matrix.</description><pubDate>Sun, 19 Jul 2026 00:00:00 GMT</pubDate><category>rlhf</category><category>llm-evaluation</category><category>data-annotation</category><category>preference-data</category></item><item><title>Best Data Annotation Tools in 2026: An Honest Guide</title><link>https://tagaroo.ai/blog/best-data-annotation-tools/</link><guid isPermaLink="true">https://tagaroo.ai/blog/best-data-annotation-tools/</guid><description>Compare the best data annotation tools of 2026 by open-source status, pricing, modalities, and workforce—one master table, honest picks by use case.</description><pubDate>Sun, 19 Jul 2026 00:00:00 GMT</pubDate><category>data annotation</category><category>data labeling</category><category>annotation tools</category><category>machine learning</category></item><item><title>Clinical Transcript Annotation Tools: A Buyer&apos;s Shortlist</title><link>https://tagaroo.ai/blog/clinical-transcript-annotation-tools/</link><guid isPermaLink="true">https://tagaroo.ai/blog/clinical-transcript-annotation-tools/</guid><description>Which clinical transcript annotation tool fits psychiatric, therapy, or SLP research? Compare options on HIPAA, speaker roles, severity scales, and IRR.</description><pubDate>Sun, 19 Jul 2026 00:00:00 GMT</pubDate><category>clinical annotation</category><category>HIPAA</category><category>qualitative software</category><category>annotation tools</category></item><item><title>CVAT vs Labelbox vs V7: Computer-Vision Tools (2026)</title><link>https://tagaroo.ai/blog/cvat-vs-labelbox-vs-v7/</link><guid isPermaLink="true">https://tagaroo.ai/blog/cvat-vs-labelbox-vs-v7/</guid><description>CVAT vs Labelbox vs V7 for computer-vision annotation: open-source vs enterprise, auto-labeling, DICOM, and pricing models, compared. See which fits.</description><pubDate>Sun, 19 Jul 2026 00:00:00 GMT</pubDate><category>annotation-tools</category><category>computer-vision</category><category>image-annotation</category><category>comparison</category></item><item><title>Ekman vs Plutchik: Which Emotion Model to Label With?</title><link>https://tagaroo.ai/blog/ekman-vs-plutchik/</link><guid isPermaLink="true">https://tagaroo.ai/blog/ekman-vs-plutchik/</guid><description>Ekman vs Plutchik for annotation: 6 discrete emotions or 8 with intensities and dyads, and why more categories lower agreement. See which to pick.</description><pubDate>Sun, 19 Jul 2026 00:00:00 GMT</pubDate><category>emotion</category><category>affect</category><category>nlp</category><category>annotation</category></item><item><title>Label Studio vs Prodigy vs doccano (2026 Comparison)</title><link>https://tagaroo.ai/blog/label-studio-vs-prodigy-vs-doccano/</link><guid isPermaLink="true">https://tagaroo.ai/blog/label-studio-vs-prodigy-vs-doccano/</guid><description>Label Studio vs Prodigy vs doccano for text annotation: open-source status, active learning, team QA, and pricing model, compared. See which one fits.</description><pubDate>Sun, 19 Jul 2026 00:00:00 GMT</pubDate><category>annotation-tools</category><category>open-source</category><category>nlp</category><category>comparison</category></item><item><title>MADRS vs HAM-D: Which Depression Scale Should You Use?</title><link>https://tagaroo.ai/blog/madrs-vs-hamd/</link><guid isPermaLink="true">https://tagaroo.ai/blog/madrs-vs-hamd/</guid><description>MADRS vs HAM-D compared: items, somatic weighting, sensitivity to change, score conversion, and remission cutoffs. See which depression scale to use.</description><pubDate>Sun, 19 Jul 2026 00:00:00 GMT</pubDate><category>depression</category><category>rating scales</category><category>clinical trials</category></item><item><title>The Best NVivo Alternatives for Qualitative Research 2026</title><link>https://tagaroo.ai/blog/nvivo-alternatives/</link><guid isPermaLink="true">https://tagaroo.ai/blog/nvivo-alternatives/</guid><description>Looking for an NVivo alternative? Compare the best free, modern, and AI-guided qualitative coding tools by price, platform, and clinical fit.</description><pubDate>Sun, 19 Jul 2026 00:00:00 GMT</pubDate><category>qualitative analysis</category><category>caqdas</category><category>nvivo</category><category>comparison</category></item><item><title>Open-Source vs Commercial Annotation Tools: 2026 Guide</title><link>https://tagaroo.ai/blog/open-source-vs-commercial-annotation/</link><guid isPermaLink="true">https://tagaroo.ai/blog/open-source-vs-commercial-annotation/</guid><description>Open-source vs commercial annotation tools: the real trade-offs in total cost, support, compliance, and lock-in, plus a decision framework for your team.</description><pubDate>Sun, 19 Jul 2026 00:00:00 GMT</pubDate><category>annotation-tools</category><category>open-source</category><category>data-quality</category><category>comparison</category></item><item><title>Qualitative Data Analysis Software Comparison (2026)</title><link>https://tagaroo.ai/blog/qualitative-analysis-software-compared/</link><guid isPermaLink="true">https://tagaroo.ai/blog/qualitative-analysis-software-compared/</guid><description>A fair, four-way qualitative data analysis software comparison of NVivo, ATLAS.ti, MAXQDA and Dedoose: pricing, AI coding and built-in IRR, side by side.</description><pubDate>Sun, 19 Jul 2026 00:00:00 GMT</pubDate><category>caqdas</category><category>qualitative-analysis</category><category>comparison</category><category>qda-software</category></item><item><title>The Brief Psychiatric Rating Scale (BPRS), Explained</title><link>https://tagaroo.ai/blog/brief-psychiatric-rating-scale-bprs/</link><guid isPermaLink="true">https://tagaroo.ai/blog/brief-psychiatric-rating-scale-bprs/</guid><description>The Brief Psychiatric Rating Scale (BPRS) rates psychopathology on 7-point items. See the scoring, the factor structure, BPRS vs PANSS, and how to code it.</description><pubDate>Wed, 15 Jul 2026 00:00:00 GMT</pubDate><category>psychosis</category><category>rating scales</category><category>schizophrenia</category></item><item><title>Cognitive Distortions List: 10 CBT Patterns, Coded</title><link>https://tagaroo.ai/blog/cbt-cognitive-distortions-list/</link><guid isPermaLink="true">https://tagaroo.ai/blog/cbt-cognitive-distortions-list/</guid><description>The cognitive distortions list, explained: all 10 CBT thinking errors with definitions and examples, where the list came from, and why they resist coding.</description><pubDate>Wed, 15 Jul 2026 00:00:00 GMT</pubDate><category>cbt</category><category>cognition</category><category>depression</category><category>annotation</category></item><item><title>Ekman&apos;s Basic Emotions: The Six, and How to Label Them</title><link>https://tagaroo.ai/blog/ekman-basic-emotions/</link><guid isPermaLink="true">https://tagaroo.ai/blog/ekman-basic-emotions/</guid><description>Ekman&apos;s basic emotions are six universal states: anger, disgust, fear, happiness, sadness, surprise. See why they anchor most emotion datasets.</description><pubDate>Wed, 15 Jul 2026 00:00:00 GMT</pubDate><category>emotion</category><category>affect</category><category>nlp</category><category>annotation</category></item><item><title>The Empathic Communication Coding System, Explained</title><link>https://tagaroo.ai/blog/empathic-communication-coding-system-eccs/</link><guid isPermaLink="true">https://tagaroo.ai/blog/empathic-communication-coding-system-eccs/</guid><description>The Empathic Communication Coding System codes empathy in clinical talk as a two-step sequence: patient opportunities, then a 0–6 provider response ladder.</description><pubDate>Wed, 15 Jul 2026 00:00:00 GMT</pubDate><category>empathy</category><category>healthcare communication</category><category>consultation</category><category>annotation</category></item><item><title>Hamilton Anxiety Rating Scale: Psychic vs Somatic HAM-A</title><link>https://tagaroo.ai/blog/hamilton-anxiety-scale-hama/</link><guid isPermaLink="true">https://tagaroo.ai/blog/hamilton-anxiety-scale-hama/</guid><description>The Hamilton Anxiety Rating Scale (HAM-A) is a 14-item clinician measure. See the psychic and somatic items, the scoring, the cutoffs, and how to code it.</description><pubDate>Wed, 15 Jul 2026 00:00:00 GMT</pubDate><category>anxiety</category><category>rating scales</category><category>clinical trials</category></item><item><title>Labov and Waletzky Narrative Analysis: The Six Parts</title><link>https://tagaroo.ai/blog/labov-waletzky-narrative-structure/</link><guid isPermaLink="true">https://tagaroo.ai/blog/labov-waletzky-narrative-structure/</guid><description>Labov and Waletzky narrative analysis breaks an oral story into six parts. See each part, why evaluation is the heart, and how reliably it can be coded.</description><pubDate>Wed, 15 Jul 2026 00:00:00 GMT</pubDate><category>narrative</category><category>discourse</category><category>sociolinguistics</category><category>speech-language pathology</category><category>annotation</category></item><item><title>The NICHD Protocol: Coding Interviewer Prompt Types</title><link>https://tagaroo.ai/blog/nichd-protocol-prompt-types/</link><guid isPermaLink="true">https://tagaroo.ai/blog/nichd-protocol-prompt-types/</guid><description>The NICHD protocol classifies a forensic interviewer&apos;s prompts from open to suggestive. See the five types, why open prompts win, and how to code them.</description><pubDate>Wed, 15 Jul 2026 00:00:00 GMT</pubDate><category>forensic</category><category>child interview</category><category>investigative interviewing</category><category>annotation</category></item><item><title>The OPTION Scale: Measuring Shared Decision-Making</title><link>https://tagaroo.ai/blog/option-shared-decision-making-scale/</link><guid isPermaLink="true">https://tagaroo.ai/blog/option-shared-decision-making-scale/</guid><description>The OPTION scale rates how far a clinician involves the patient in a decision. See the behaviors it scores, why real scores are low, and how to code it.</description><pubDate>Wed, 15 Jul 2026 00:00:00 GMT</pubDate><category>shared decision making</category><category>healthcare communication</category><category>consultation</category><category>annotation</category></item><item><title>Plutchik&apos;s Wheel of Emotions: 8 Primaries, Explained</title><link>https://tagaroo.ai/blog/plutchik-wheel-of-emotions/</link><guid isPermaLink="true">https://tagaroo.ai/blog/plutchik-wheel-of-emotions/</guid><description>Plutchik&apos;s wheel of emotions maps eight primaries into four opposing pairs, with intensity rings and dyads. See how to label emotion in text.</description><pubDate>Wed, 15 Jul 2026 00:00:00 GMT</pubDate><category>emotion</category><category>affect</category><category>nlp</category><category>annotation</category></item><item><title>Poverty of Speech and Alogia: Coding the SANS Subscale</title><link>https://tagaroo.ai/blog/sans-alogia-poverty-of-speech/</link><guid isPermaLink="true">https://tagaroo.ai/blog/sans-alogia-poverty-of-speech/</guid><description>Poverty of speech is the core of alogia. See the four SANS alogia items, how they differ from poverty of content, and how to code each from a transcript.</description><pubDate>Wed, 15 Jul 2026 00:00:00 GMT</pubDate><category>negative symptoms</category><category>schizophrenia</category><category>psychosis</category><category>speech</category><category>alogia</category></item><item><title>Positive Formal Thought Disorder: The SAPS Subscale</title><link>https://tagaroo.ai/blog/saps-positive-thought-disorder/</link><guid isPermaLink="true">https://tagaroo.ai/blog/saps-positive-thought-disorder/</guid><description>Positive formal thought disorder is disorganized speech you can see. Meet the SAPS FTD items, how they differ from the TLC and SANS, and how to code them.</description><pubDate>Wed, 15 Jul 2026 00:00:00 GMT</pubDate><category>thought disorder</category><category>schizophrenia</category><category>psychosis</category><category>speech</category><category>disorganization</category></item><item><title>Searle&apos;s Speech Acts: The Five Illocutionary Types</title><link>https://tagaroo.ai/blog/searle-speech-acts-taxonomy/</link><guid isPermaLink="true">https://tagaroo.ai/blog/searle-speech-acts-taxonomy/</guid><description>Searle&apos;s speech acts sort every utterance into five illocutionary types. See the classes, direction of fit, examples, and the tie to dialogue-act tagging.</description><pubDate>Wed, 15 Jul 2026 00:00:00 GMT</pubDate><category>pragmatics</category><category>linguistics</category><category>speech acts</category><category>nlp</category><category>annotation</category></item><item><title>The Thought Language and Communication Scale, Explained</title><link>https://tagaroo.ai/blog/tlc-thought-language-communication-scale/</link><guid isPermaLink="true">https://tagaroo.ai/blog/tlc-thought-language-communication-scale/</guid><description>The Thought Language and Communication scale (TLC) is Andreasen&apos;s glossary of disordered speech. See the items, the reliability data, and how to code them.</description><pubDate>Wed, 15 Jul 2026 00:00:00 GMT</pubDate><category>thought disorder</category><category>schizophrenia</category><category>psychosis</category><category>speech</category></item><item><title>The Toulmin Model of Argumentation: The Six Elements</title><link>https://tagaroo.ai/blog/toulmin-model-of-argumentation/</link><guid isPermaLink="true">https://tagaroo.ai/blog/toulmin-model-of-argumentation/</guid><description>The Toulmin model of argumentation breaks an argument into six functional parts. See the elements, a worked example, and why warrants are hardest to code.</description><pubDate>Wed, 15 Jul 2026 00:00:00 GMT</pubDate><category>argumentation</category><category>rhetoric</category><category>reasoning</category><category>nlp</category><category>annotation</category></item><item><title>Cohen&apos;s Kappa: How to Compute and Interpret Agreement</title><link>https://tagaroo.ai/blog/cohens-kappa-inter-rater-reliability/</link><guid isPermaLink="true">https://tagaroo.ai/blog/cohens-kappa-inter-rater-reliability/</guid><description>How to compute Cohen&apos;s kappa and weighted kappa by hand, read the result honestly, and know when to use Krippendorff&apos;s alpha, ICC, or Gwet&apos;s AC1 instead.</description><pubDate>Mon, 13 Jul 2026 00:00:00 GMT</pubDate><category>reliability</category><category>statistics</category><category>kappa</category></item><item><title>The GAD-7 Anxiety Scale: Items, Cutoffs &amp; Transcript Coding</title><link>https://tagaroo.ai/blog/gad-7-anxiety-scale/</link><guid isPermaLink="true">https://tagaroo.ai/blog/gad-7-anxiety-scale/</guid><description>The GAD-7 is a seven-item anxiety screen. See the 5/10/15 severity bands, the ≥10 cutoff, and how to code it from an interview transcript.</description><pubDate>Mon, 13 Jul 2026 00:00:00 GMT</pubDate><category>anxiety</category><category>rating scales</category><category>screening</category></item><item><title>Gottschalk-Gleser Content Analysis: Coding Affect in Speech</title><link>https://tagaroo.ai/blog/gottschalk-gleser-content-analysis/</link><guid isPermaLink="true">https://tagaroo.ai/blog/gottschalk-gleser-content-analysis/</guid><description>How Gottschalk-Gleser content analysis scores affect in speech, clause by clause—the seven depression subscales, the weighted coding, and its NLP legacy.</description><pubDate>Mon, 13 Jul 2026 00:00:00 GMT</pubDate><category>content analysis</category><category>depression</category><category>methods</category><category>psycholinguistics</category></item><item><title>Hamilton Depression Rating Scale (HAM-D): A Coding Guide</title><link>https://tagaroo.ai/blog/hamilton-depression-rating-scale-hamd/</link><guid isPermaLink="true">https://tagaroo.ai/blog/hamilton-depression-rating-scale-hamd/</guid><description>How the Hamilton Depression Rating Scale (HAM-D) is scored and coded: the 17 items, the 0–52 range, remission and response cutoffs, and its known flaws.</description><pubDate>Mon, 13 Jul 2026 00:00:00 GMT</pubDate><category>depression</category><category>rating scales</category><category>clinical trials</category></item><item><title>MADRS Explained: 10 Items, Scoring Bands &amp; Remission</title><link>https://tagaroo.ai/blog/madrs-depression-scale/</link><guid isPermaLink="true">https://tagaroo.ai/blog/madrs-depression-scale/</guid><description>MADRS scoring explained: the 10 clinician-rated items, the 0-6 anchors, severity bands, and the response and remission cutoffs. See how it&apos;s coded.</description><pubDate>Mon, 13 Jul 2026 00:00:00 GMT</pubDate><category>depression</category><category>rating scales</category><category>clinical trials</category></item><item><title>PHQ-9 Scoring Explained: Items, Cutoffs, and Coding</title><link>https://tagaroo.ai/blog/phq-9-scoring-guide/</link><guid isPermaLink="true">https://tagaroo.ai/blog/phq-9-scoring-guide/</guid><description>PHQ-9 scoring made clear: what the nine items measure, the 0-3 anchors, the severity bands, the ≥10 cutoff, and how to code a PHQ-9 from an interview.</description><pubDate>Mon, 13 Jul 2026 00:00:00 GMT</pubDate><category>depression</category><category>rating scales</category><category>screening</category></item><item><title>Welcome to the Tagaroo blog: annotation, done right</title><link>https://tagaroo.ai/blog/welcome/</link><guid isPermaLink="true">https://tagaroo.ai/blog/welcome/</guid><description>Field notes on annotation—inter-rater reliability, clinical rating scales, qualitative coding, and running labeling projects that actually hold up.</description><pubDate>Mon, 13 Jul 2026 00:00:00 GMT</pubDate><category>meta</category></item><item><title>Young Mania Rating Scale (YMRS): Rating Mania from Speech</title><link>https://tagaroo.ai/blog/young-mania-rating-scale-ymrs/</link><guid isPermaLink="true">https://tagaroo.ai/blog/young-mania-rating-scale-ymrs/</guid><description>The Young Mania Rating Scale (YMRS) explained: its 11 items, the four double-weighted items, severity bands, and how to rate mania from interview speech.</description><pubDate>Mon, 13 Jul 2026 00:00:00 GMT</pubDate><category>mania</category><category>bipolar</category><category>rating scales</category></item></channel></rss>