Reconsidering reconsent: Threats to internal and external validity when participants reconsent after debriefing
Bibliographic record
Abstract
We overwhelmingly utilize (partially) informed consent for, and debriefing of, human research participants. Also common is the practice of reconsent, particularly where changes in study protocols (or in participants themselves) occur midstream - participants consent again to remaining in the project or to having their data included. Worryingly under-discussed is post-debriefing reconsent, wherein participants can withdraw their data after learning more fully of the study's goals and methods. Yet, major ethics bodies in Canada, the United States and the United Kingdom promote such practice, with vague and potentially problematic guidelines. Here, the author provides examples involving such reconsent practice, highlighting potentially serious problems that are scientific (e.g. threats to internal and external validity) and ethical (i.e. to the participant, their peers, the researcher and society) in nature. Particularly, problematic is the introduction, by design, of unknowable bias in our research findings. For example, highly prejudiced participants could withdraw data from a discrimination study after learning of the study's hypotheses and goals. The practice may arguably contradict an Open Science goal of increasing research transparency. This call for discussion about the direction of psychological science methods aims to engage a broader discussion in the research community.
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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 | 0.606 | 0.823 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier 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".