Bibliographic record
Abstract
Prompted by shared discussions about our doctoral research, this paper focuses on two tensions we identified when applying to our university’s Institutional Review Board (IRB). The first tension relates to our discomfort with the assumptions about research participants as articulated in the IRB application. We detail how one of us sought to work with/in but also outside of the constraints we discuss. The second tension takes us into a more experimental space. We write ‘outside’ of the IRB boxes as a form of critique, but also as a way to produce more affirmative ways of thinking about what else can be thought and done within university IRB structures. We focus in particular on the ways that “data” is contained within IRB boxes. We conclude by offering some additional questions that this process of thinking/writing together have generated.
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 | 0.604 | 0.667 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.035 | 0.076 |
| Scholarly communication | 0.047 | 0.034 |
| Open science | 0.008 | 0.030 |
| Research integrity | 0.015 | 0.040 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".