Institutional Conflicts of Interest: Protecting Human Subjects, Scientific Integrity, and Institutional Accountability
Bibliographic record
Abstract
If clinical trials become a commercial venture in which self-interest overrules public interest and desire overrules science, then the social contract which allows research on human subjects in return for medical advances is broken. Background In the past two decades, the involvement of non-academic sponsors of biomedical research, particularly clinical trial research, has increased exponentially. The value of such sponsored research is difficult to ascertain. However, it is estimated that, between 1980 and 2003, overall research and development expenditures by US pharmaceutical companies increased from $2 billion to $33 billion and that, in 2001, clinical trial research expenditures in Canada totaled $800 million to $1 billion. The source of funding for biomedical research has shifted significantly from predominantly government and private foundations to industry. By 2002,70% of funding for clinical trials came from industry. These factors have affected the conduct of research, particularly clinical trial research, in a variety of ways.
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 | 0.162 | 0.254 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.074 |
| Scholarly communication | 0.028 | 0.020 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.031 | 0.019 |
| 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".