Using Annotation for Transparent Inquiry (ATI) to Teach Qualitative Research Methods
Bibliographic record
Abstract
In political science, qualitative analytic methods are rarely taught using “active learning” strategies. We discuss a novel approach to teaching such methods: having students engage with scholarship that has been annotated using Annotation for Transparent Inquiry (ATI). ATI allows authors to annotate passages in a digital publication to clarify methodology, add detail about evidence or analysis, or link to data sources. Learning methods through engagement with annotated articles allows students to interact with original data and to better understand and evaluate how authors collected, analyzed, and used those data. This leads students to learn research methods in a way that more closely approximates how they will use those methods in their own research. We present a general description of strategies for teaching with ATI. We illustrate the approach using three examples of instructors teaching both undergraduate and graduate students. We conclude with recommendations for effectively using ATI in the classroom.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.103 | 0.215 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.016 | 0.009 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".