Bibliographic record
Abstract
Disasters such as flash flooding, mass shootings, and train and airplane accidents involving large numbers of victims produce significant opportunity for research in the biosciences. This opportunity exists in the extreme tails of life events, however, during which decisions about life and death, valuing and foregoing, speed and patience, trust and distrust, are tested simultaneously and abundantly. The press and urgency of these scenarios may also challenge the ability of researchers to comprehensively deliver information about the purposes of a study, risks, benefits, and alternatives. Under these circumstances, we argue that acquiring consent for the immediate use of data that are not time sensitive represents a gap in the protection of human study participants. In response, we offer a two-tiered model of consent that allows for data collected in real-time to be held in escrow until the acute post-disaster window has closed. Such a model not only respects the fundamental tenet of consent in research, but also enables such research to take place in an ethically defensible manner.
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 | 0.146 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.051 |
| Scholarly communication | 0.011 | 0.023 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.021 | 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".