Restoring the Integrity of the Pharmaceutical Science Record: Two Tales of Transparency
Bibliographic record
Abstract
Inappropriate prescription and overconsumption of pharmaceuticals is one of the most pressing public health concerns in North America. Aggressive pharmaceutical promotion practices are widely recognized as a major contributing factor. This article for the online law scholars community journal JOTWELL discusses two recent medical journal articles that provide further evidence of serious problems with the scientific record that has become an intrinsic part of pharmaceutical marketing. They document each in their own way the corruption of scientific practices in which academic scientists appear to play a significant role, but also indicate how the scientific community and civil society can help correct the record and expose misconduct. The papers further illustrate how legal tools can enable them to do so. They both affirm the importance of transparency and show each in their own way how transparency can be obtained or promoted. The paper discusses briefly the lessons to be learned from these informative publications.
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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 | 0.080 | 0.246 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.013 | 0.060 |
| Scholarly communication | 0.047 | 0.050 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.023 | 0.034 |
| Insufficient payload (model declined to judge) | 0.005 | 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".