Institutional Review boards: Ethics, regulations and the research agenda
Bibliographic record
Abstract
Abstract Information Science researchers and designers are well‐positioned to become active participants in scholarly and institutional conversations concerning the protection of human subjects. The overall goals of this panel are: 1) to extend the discourse within the Information Science field concerning the protection of human subjects in research; 2) to explore new ways to improve the relationship between researchers and Institutional Review Boards; and 3) to advance current Institutional Review Board policies and procedures concerning the use of iterative, culturally appropriate, qualitative methods within social science research. This panel will stimulate conversations through which “IRBs and investigators accept their common charge to meet the needs of subjects and to improve the quality of research.” (Burke, , p. 921)
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 | 0.589 | 0.544 |
| Meta-epidemiology (narrow) | 0.001 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.013 | 0.025 |
| Scholarly communication | 0.045 | 0.017 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.037 | 0.031 |
| Insufficient payload (model declined to judge) | 0.013 | 0.011 |
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".