Social Workers, Race Discrimination and International Human Rights Conventions: A Canadian Perspective
Bibliographic record
Abstract
Racial discrimination continues to haunt Canada, calling for effective and new solutions. There are clear and real limitations to the current domestic avenues of redress. This paper reviews the effectiveness of the International Convention on the Elimination of All Forms of Racial Discrimination. We argue that the treaty contains comprehensive and legally effective provisions to combat racial discrimination. Social workers, along with other professionals, should engage with the international legal regime to assist their clientele to combat racial discrimination. Internationally, progress toward racial equality has been made in the last two decades, symbolized partly by the collapse of the apartheid regime in South Africa. But the belief that racism and racial discrimination are very much under control is as erroneous as it is pervasive (Tang, 2003). Xenophobic and racially motivated acts of violence continue to plague people in all parts of the world. In the United States, the fact remains that racial discrimination is deeply entrenched, characterized by disproportionate incarceration of blacks, police violence, and poverty (Gordon, 2000). Likewise, racial discrimination in Canada is more than isolated instances of racist behavior by aberrant individuals or the acts of extremist groups. Scholars like Anand (1998) find much evidence of racism and discrimination in Canadian society that includes government-sanctioned discrimination as well as racial hatred.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.045 | 0.040 |
| Scholarly communication | 0.021 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".