Medical selection upon hiring and the applicant’s right to lie about his health status
Bibliographic record
Abstract
As democracies respecting human rights, France and Quebec both prohibit discrimination in hiring on the grounds of disabilities. On the other hand, businesses wish to select the most effective job applicants possible in light of the physical and psychological demands of the job. In order to avoid being eliminated from the selection process, applicants may be tempted to hide or even lie about their health status. Consequently, the law has been put to the test, seeking a delicate balance regarding the consequences of applicants’ silence or false declarations concerning their health status. The legal consequences of this situation have been viewed differently in France and Quebec. In Quebec, contractual synallagmatic obligations appear to take precedence over rules limiting the collection of discriminatory information, allowing for a selection that is nevertheless prohibited by the laws protecting human rights. By contrast, in France, the employer has no right to intrude in matters of worker health and the withholding of information or even lies on the part of applicants can only rarely be used to justify a dismissal. This interpretation poses great challenges in view of the “safety obligation of result” that is imposed on the French employer. Through a comparative analysis of French and Quebec positive law, this paper explores the limits of the employer’s ability to investigate an applicant’s health during the hiring process. It then turns to the question of the right to lie as a way to avoid being discriminated against on the basis of disability and the consequences of this right on the employment relationship
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".