Bibliographic record
Abstract
Renu Addlakha (RA) discusses a number of pertinent ethical issues in the summary of her research (1).These issues could be generally divided into three categories: confidentiality, freedom of participation and consent, and the therapeutic misconception. ConfidentialityRA stated that she had been given permission by the institution to review patient charts.She correctly noted that while this permission may have given her legal access to the charts it did not absolve her from the ethical need to obtain explicit consent from patients and/or their families to use the files.RA assured study subjects that information provided to her would be kept strictly confidential.Is it possible that in some instances, for example, if a subject were to indicate that she had abused a child, there would be specific legal requirements to report such information to the appropriate authorities?In Canada we can rarely promise absolute confidentiality and a similar constraint may apply to researchers in India.
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 | 0.107 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.022 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.025 | 0.028 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".