Action Research, Ethics, the Politics of Academia, and People
Bibliographic record
Abstract
Around ten years ago, sometime during my early tenure as the Chair of our little liberal arts university's Research Ethics Board (REB), and before the creation of a second committee focused specifically on undergraduate research, I had the minor duty to make sure every department Chair completed a form indicating the amount and nature of work being accomplished by their students involving human participants.In all honesty, I really did not have to do that much because of the superb staff of our Research Office that was much more diligent about these types of things than I was, or am (NB: They still chase me down with kindness to fill out my personal research ethics reports).They would send out electronic forms to department Chairs and follow up reminders to complete said forms in a timely manner.The documents were simple, straightforward, and really were nothing complicated.Basically, in those days, they were one page, fill in the blank documents designed to simply track undergraduate research at the university.The majority of Chairs sent back the forms in short time, but for the minority that missed, overlooked or ignored the requests, I would then be asked by staff to follow-up with a personal reminder.I assumed the thought was that an email by faculty would get more attention than staff, and although the tactic somewhat contradicted the university's stated exterior of collegiality amongst all members in a respectful work environment, my emails typically did get a quicker response than theirs.
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 | 0.130 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.024 | 0.212 |
| Scholarly communication | 0.043 | 0.037 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.015 | 0.018 |
| Insufficient payload (model declined to judge) | 0.007 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".