Bibliographic record
Abstract
Dr. H is an expert on the treatment of depression. A pharmaceutical company, Calaxy Inc. signed a contract with Dr. H and his institution for a multisite three-year study on the efficacy and safety of a new antidepressant, Xanadu, for use in pregnant women. The contract stipulates that Dr. H will have access to all data for final analysis and that all publications based on the study will be submitted for final approval to the sponsor before public disclosure. Dr. H's budget includes money for finder's fees for clinicians who recruit patients into the trial and rewards for clinician–researchers whose patients remain in the trial for the duration of their pregnancy. In the course of the trial, Dr. H becomes worried about potential negative effects of Xanadu on newborns. He reveals his concern to the company, requests immediate access to all the data, and indicates that he will reveal his concerns at an upcoming international meeting. The company refers to a contradictory opinion of an internal data-monitoring committee set up by the sponsor, refuses to provide full access to the data, and points out that researchers have to obtain final approval of the sponsor before any public discussion of the results. Shortly after, Dr. H receives from Calaxy an abstract discussing the interim results of the study, accepted for presentation at an international conference. Dr. H is first author on the abstract, which does not contain any reference to his concerns. Dr. H contacts the chair of his department, Dr. I, who is a remunerated board member of Calaxy.[…]
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 | 0.012 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.237 | 0.077 |
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".