Bibliographic record
Abstract
Abstract The emphasis in political science on procedural ethics has led to a neglect of how researchers should consider and treat study participants, from design to publication stage. This article corrects this oversight and calls for a sustained discussion of research ethics across the discipline. The article's core argument is twofold: that ethics should matter to everyone, not just those who spend extended time in the field; and that ethics is an ongoing responsibility, not a discrete task to be checked off a “to do” list. Ethics matter in all types of political science research because most political science involves “human subjects.” Producers and consumers of political science research need to contemplate the ambiguous and oftentimes uncomfortable dimensions of research ethics, lest we create a discipline that is “nonethical,” or worse, unethical.
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
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 | 0.343 | 0.290 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.017 | 0.097 |
| Scholarly communication | 0.033 | 0.019 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.032 | 0.034 |
| Insufficient payload (model declined to judge) | 0.005 | 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".