Methodological Decisions in Undertaking Academic Integrity Policy Analysis: Considerations for Future Research
Bibliographic record
Abstract
Purpose: The purpose of this article is to share details of the methodological decisions regarding data collection that a researcher or research team may want to consider when undertaking a policy analysis. Methods: We have undertaken a meticulous documentation of our decision-making processes throughout the research design process. Results: We provide narrative evidence of what worked for us as a collaborative research team. Implications: Understanding the decisions we made throughout our research design and implementation may help other research teams, particularly those working as virtual collaborations and/or those undertaking academic integrity policy analysis.
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How this classification was reachedexpand
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 | 0.844 | 0.908 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.015 | 0.038 |
| Scholarly communication | 0.033 | 0.050 |
| Open science | 0.014 | 0.016 |
| Research integrity | 0.021 | 0.028 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".