Multicultural social work in Canada : working with diverse ethno-racial communities
Bibliographic record
Abstract
Acknowledgements Preface Contributors Chapter 1 Introduction Chapter 2 Culturally Appropriate Knowledge and Skills Required for Effective Multicultural Practice with Individuals, Families, and Small Groups Chapter 3 Culturally Appropriate Social Work for Successful Community Development in Diverse Communities Chapter 4 Canadian Society: Social Policy and Ethno-Racial Diversity Chapter 5 Whiteout: Looking for Race in Canadian Social Work Practice Chapter 6 Practice with Immigrants in Quebec Section 2 Personal Narratives On Social Work with Diverse Ethno-Racial Communities Chapter 7 Social Work with Canadians of Italian Background: Applying Cultural Concepts to Bicultural and Intergenerational Issues in Clinical Practice Chapter 8 Social Work with Canadians of Arab Background: Insight into Direct Practice Chapter 9 Social Work with Canadians of Jewish Background: Guidelines for Direct Practice Chapter 10 Social Work with Canadians of Ukrainian Background: History, Direct Practice, Current Realities Chapter 11 Social Work Practice with Canadians of Aboriginal Background: Guidelines for Respectful Social Work Chapter 12 The Franco-Ontarian Community: From a Period of Resistance to New Social Solidarities and Practices Chapter 13 Social Work Practice with African Canadians: An Examination of the African-Nova Scotian Community Chapter 14 Canadians of Caribbean Background: Postcolonial and Critical Race Perspectives for Practice Chapter 15 The Context of Culture: Social Work Practice with Canadians of South Asian Background
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.060 | 0.011 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 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".