Research Integrity: Reflections on the APSA’s Qualitative Transparency Deliberations and Recommendations for Advancing Political Science Practice
Bibliographic record
Abstract
Abstract This chapter examines the findings of the Qualitative Transparency Deliberations (QTD), initiated by the American Political Science Association (APSA) in 2016, which explored the benefits and challenges of transparency in qualitative research. The chapter emphasizes that transparency is not an end in itself but a tool for safeguarding research integrity by clarifying research goals, methods, and the evidence-generation process. While the QTD encouraged openness, it also acknowledged the limitations of transparency, particularly regarding data-sharing in sensitive research contexts. The chapter also highlights the risks, such as ethical concerns and power imbalances, which can compromise participant safety and researcher integrity. Nonetheless, it advocates for explicitness in research practices to enhance understanding, validity, and intellectual rigour. The QTD findings offer a framework for editors, reviewers, and funders to develop evaluative criteria that respect diverse research traditions while placing the responsibility for balancing transparency and ethical obligations with individual researchers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | MetaresearchResearch integrity Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | MetaresearchResearch integrity Domain: Methods · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | 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 | 0.582 | 0.565 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.027 | 0.084 |
| Scholarly communication | 0.050 | 0.045 |
| Open science | 0.011 | 0.039 |
| Research integrity | 0.050 | 0.091 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".