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Record W1483857384 · doi:10.1300/j051v12n03_03

Internationalizing Anti-Racism Efforts

2003· article· en· W1483857384 on OpenAlexaff
Kwong‐leung Tang

Bibliographic record

VenueJournal of Ethnic & Cultural Diversity in Social Work · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsUniversity of Northern British Columbia
FundersCentre for Engineering Research and Development
KeywordsRacismTreatyConventionSocial workPolitical scienceConvention on the Elimination of All Forms of Discrimination Against WomenLawGovernment (linguistics)Human rightsInternational human rights law

Abstract

fetched live from OpenAlex

Racial discrimination continues to haunt our societies, calling for sustained and new solutions. In 1994, the US government signed the International Convention on the Elimination of All Forms of Racial Discrimination. Three years later, it ratified this international agreement. This article reviews the effectiveness of this United Nations Convention and discusses its main provisions: national reporting and the individual communications procedure. It finds that the treaty contains comprehensive and legally effective provisions to combat racial discrimination and argues that social workers, along with other professionals, should engage with the international legal regime to assist their clientele to combat racial discrimination. Social workers have a number of roles: advocate, educator, service provider and broker. Their involvement in such an international legal regime would have an added significance; it has the potential to expand the domain of international social work as well as overcome the limits of domestic action against racial discrimination.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0060.005
Scholarly communication0.0080.003
Open science0.0010.008
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0100.001

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.

Opus teacher head0.133
GPT teacher head0.415
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2003
Admission routes1
Has abstractyes

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