Codes and Complaints in Context: Historical, Empirical, and Actuarial Foundations
Bibliographic record
Abstract
This chapter describes the creation and evolution of the ethics codes of the American Psychological Association and the Canadian Psychological Association. It examines the four major mechanisms that hold therapists and counselors accountable to explicit standards: professional ethics committees, state licensing boards, civil (e.g., malpractice) courts, and criminal courts. It describes the major reasons that therapists are sued or face formal ethics or licensing complaints according to multiyear actuarial data for psychology malpractice suits, ethics complaints, and licensing complaints in the United States and Canada. It discusses trends in formal complaints against psychologists and discusses differences between the United States and Canada in this regard. It emphasizes the importance of not confusing ethical behavior with what keeps us out of trouble with these review agencies and how easily our sense of what is ethical can run through a reductionistic mill and become “avoiding detection,” “eliminating risk,” or “escaping accountability.”
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 | 0.015 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.007 | 0.039 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".