Applying the Meiorin Decision requirements to the fitness test for correctional officer applicants; examining adverse impact and accommodation
Bibliographic record
Abstract
The fitness test for correctional officer applicants (FITCO) was constructed a priori to conform to requirements established by the Meiorin Decision of the Supreme Court of Canada. A critical obligation from this decision is to determine whether the FITCO has the potential of adverse impact on any subpopulation of applicants and, if so, whether it is possible to provide accommodation. The FITCO pass rate was 28.6% for 56 women and 72.7% for 22 men, which indicates adverse impact on the female applicants. There was no specific adverse impact on minority applicants. To evaluate training as accommodation for adverse impact, a subgroup of 40 females and 8 males engaged in a 6-week FITCO-specific training program with pre-FITCO and post-FITCO performance evaluations. Over the 6 weeks, the overall FITCO pass rate of the females improved to 82.5%, whereas the pass rate of the males improved to 100%, indicating that the training program removed the adverse impact that the FITCO had on the females. We conclude that although the FITCO is likely to have an adverse impact on female correctional officer applicants, a 6-week FITCO-specific training program can provide the accommodation necessary to overcome the potential adverse impact, and the FITCO meets all the requirements established by the Supreme Court of Canada's Meiorin Decision.
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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 | 0.032 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".