Cross-Cultural Social Work: A Critical Approach to Teaching and Learning to Work Effectively across Intersectional Identities
Bibliographic record
Abstract
Abstract All relationships in social work education and practice constitute sites of cross-cultural exchanges. In keeping with the profession’s social justice mandate and anti-oppressive principles, it is fundamental for emerging social workers to begin the life-long learning process of developing a congruent composite of awareness, values, knowledge and skills essential for working effectively across diverse social locations and intersectional identities. Grounded in a social justice framework, this article engages critically with the concept of cultural competence in social work pedagogy, explores the significance of diversity and intersectionality in social work education and proposes a multidimensional model for teaching, learning and evaluating cross-cultural sensitivity and responsivity in the social work class-room.
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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 | 0.041 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.030 | 0.135 |
| Scholarly communication | 0.024 | 0.020 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 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".