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Record W4377140224 · doi:10.31219/osf.io/gqzwx

The Long and Winding Road to Professional Regulation in Ontario Canada

2023· preprint· en· W4377140224 on OpenAlexaffabout
Kendra Thomson, Rosemary A. Condillac, Kim Trudeau Craig, Julie Koudys, Louis Busch, Evangelo Boutsis, Joan Broto

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicBehavioral and Psychological Studies
Canadian institutionsSeneca PolytechnicCentre for Addiction and Mental HealthSt. Lawrence CollegeBrock University
Fundersnot available
KeywordsCertificationPublic relationsContext (archaeology)Political sciencePerspective (graphical)Public administrationProfessional associationField (mathematics)Public policyBusinessLawGeography

Abstract

fetched live from OpenAlex

This article describes the long and winding road to regulation of behaviour 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 behaviour 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 paper is to inspire behaviour 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. Ultimately, efforts to promote ethical, effective, and socially valid ABA services will advance our field and enhance the benefits for those we support.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.137
Threshold uncertainty score0.865

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.261
GPT teacher head0.376
Teacher spread0.115 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes2
Has abstractyes

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