The Long and Winding Road to Professional Regulation in Ontario Canada
Bibliographic record
Abstract
This article describes the long and winding road to regulation of behavior analysts in Ontario, Canada over the past 25 years. It is written from the perspective of some of the many volunteers of the professional association (Ontario Association for Behaviour Analysis) who have contributed to this goal. The information has been corroborated by historical records and publicly available information. The need for public protection and oversight of behavior analysis was noted in our field more than 50 years ago and continues to be relevant. With changes to international certification and concerns raised by some constituents about ABA practices, many jurisdictions continue to seek support for regulatory oversight. The goal of this article is to inspire behavior analysts in other jurisdictions to advocate for protection of the public and for recognition and oversight of the profession through policy reform. To this end, we have documented our collective efforts and experiences and suggested strategies that worked in our context that may generalize to other jurisdictions. In the end, efforts to promote ethical, effective, and socially valid ABA services will advance our field and enhance the benefits for those we support.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".