Mohammed Al-Waqfi United Arab Emirates University
Bibliographic record
Abstract
This paper examines the incidence of racial discrimination in employment in Canada over the last two decades (1980-1999). Using data obtained from a sample of 119 legal cases, the paper provides a quantitative and qualitative analysis of the nature of racial discrimination legal decisions as well trends in this area in Canada. Some policy recommendations to combat racial discrimination in the workplace are suggested. Canada’s population and workforce are becoming increasingly pluralistic. Forty-two percent of Canadians report origins other than French or British, while sixteen percent of Canadians are foreign born (Heritage Canada 2001). Successive census data show that Visible Minority (VM) population1 has almost trebled over the last two decades from 4.7 % in 1981 to 13.4 % in 2001. Similarly the proportion of VMs in the total labor force in Canada rose more than two-and-one-half times from 4.9 in 1981 to 12.6 % in 2001 (Census Canada). With a highly diversified workforce, it is essential that equal opportunities be available for all Canadians, including racial minorities in a non-discriminatory work environment. Canada’s economic growth and prosperity in a highly competitive and global marketplace will depend on full utilization of the talents, skills, knowledge, and energy of all Canadians.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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".