Limited confidentiality, academic freedom, and matters of conscience: Where does CPA stand?
Bibliographic record
Abstract
Normally, we would not request an opportunity to reply to another author’s response (Zinger, Wichmann, and Gendreau 2001) to our commentary (Palys and Lowman 2001) on one of their articles (Zinger, Wichmann, and Andrews 2001). The purpose of this brief article is two-fold: (a) to clarify our position on limited confidentiality; (b) to address some fundamental (and more general) questions about the Canadian Psychological Association’s (CPA’s) approach to confidentiality. First, we clarify our position and then examine issues arising from the obligations that Zinger and his colleagues cite to justify the limitations on confidentiality they employed in their research on prisoners.
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 | 0.023 | 0.104 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.054 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.028 | 0.037 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".