Currents in Contemporary Ethics
Bibliographic record
Abstract
An increasing number of community physicians are involved in clinical research.Indeed, 60 of industry-funded research is now spent on community based trials. This surge in community based clinical trials has increased the number of clinical trials applications submitted to the drug regulatory agencies by pharmaceutical sponsors. Many have argued that the commercial interests connected to the conduct and outcome of these trials also increases the potential for conflicts of interest for participating physicians. The context in which these trials take place increases the potential for a host of practices that infringe on ethical, legal and clinical obligations of physicians For example, financial recruitment incentives may lead to violations of the inclusion criteria and the consent process. It may result in inappropriate recruitment of patient participants and a blurring of the ethically significant distinctions between treatment and research. In some cases, it may be hard to distinguish research from the marketing of new products and attempts to influencing prescribing patterns.
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 | 0.027 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.076 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.017 | 0.026 |
| Insufficient payload (model declined to judge) | 0.010 | 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".