Empirical Research on Ethical Issues in Pediatric Research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Although there is usually agreement about the ethical principles that should govern research on children, there may be little agreement on how those principles should be interpreted into research procedures in some instances. Empirical research on ethical issues that arise in research on children can often elucidate ways to improve on existing research practices and ways to resolve debates about best practices. Following in the success of evidence-based medicine, evidence-based ethical problem solving in human research can enable investigators to avoid such poor alternatives as doing nothing, endlessly debating, or acting on the basis of hunch or time-honored but dubious research practices. A variety of approaches to evidence-based ethical problem solving are illustrated in this article. Keywords: empirical researchevidence-based ethical problem solving Notes 1A description of the conference appears at http://www.csueastbay.edu/JERHRE/ conference/index.html. Conference panelists included such highly qualified agency representatives as Bernard Schwetz, director of the U.S. Office of Protection of Human Research Participants, and David Lepay, U.S. Food and Drug Administration, and academic representatives such as Robert Boruch (University of Pennsylvania), Norman Bradburn (University of Chicago), Henry Dinsdale (Queens College, Canada), Susan Fish (Boston University), Mark Frankel (American Association for the Advancement of Science), Greg Koski (Harvard), Felice Levine (American Educational Research Association), and Robert Levine (Yale). See http://www.csueastbay.edu/JERHRE/ for the complete list of experts who discussed these cases with the audience. A summary of some of the cases and conclusions appears in the March 2007 issue of JERHRE; see http://caliber.ucpress. net/loi/jer.
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.
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 | 0.029 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.004 | 0.054 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it