Human Rights and the Duty to Accommodate in Employment: Stakeholders' Knowledge and Attitudes.
Bibliographic record
Abstract
The purpose of this study was to measure the level of knowledge and the type of attitudes of key stakeholders on human rights and the duty to accommodate in employment in Canada. Two survey instruments, a 20 item true or false knowledge questionnaire and a 20 item seven point Likert scale attitudes questionnaire, developed by the researcher based on literature review, were administered to 160 participants. Both quantitative and qualitative analyses were utilized for this study. Among all participants, the results indicated a general low level of knowledge with a mean score of 11.20 (SD = 2.317) and slightly positive attitudes with a mean score of 90.17 (SD = 14.098) on human rights and the duty to accommodate. Specifically, analysis indicated participants in the human resource occupation, in higher income brackets, working in larger organizations, in a unionized environment and in the public sector have more knowledge than their counterparts. There was also statistical significance for participants in the higher income brackets and working in a unionized environment demographic on the attitudes questionnaire. With respect to correlation factors, both academic attainment/knowledge level and knowledge/attitudes were slightly positively correlated at a statistically significant level. However, in both cases the coefficient of determination (R²) was relatively low at 0.021 and 0.068 respectively. Therefore, the variability of knowledge based on academic attainment and attitudes based on knowledge share 2.1% and 6.8% respectively. In essence, 97.9% and 93.2% of variability can be accounted for by other variables. --P. ii.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".