Plumbing the Depths of Ethical Payment for Research Participation
Bibliographic record
Abstract
This article refers to:Promoting Ethical Payment in Human Infection Challenge StudiesPromoting Ethical Payments in Human Challenge Studies Conducted in LMICs: Are We Asking the Right Questions?A Call for Radical Transparency regarding Research PaymentsPaying for Fairness? Incentives and Fair Subject SelectionResearch Participants Should Be Rewarded Rather than "Compensated for Time and Burdens"Considering the Importance of Context for Ethical Practice on Reimbursement, Compensation and Incentives for Volunteers in Human Infection Controlled StudiesPaying the Right Amount to Challenge Trial Participants – We Need to Use Behavioral Science Insights to Sell What's RightWhat Fairness Demands: How We Can Promote Fair Compensation in Human Infection Challenge Studies and BeyondTime to Professionalize Service to Research? Pay Nothing or Full Wage for Labor
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.061 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.011 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.056 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".