Unsettling Discourses of Cross-Cultural Social Work Practice: Moving from Theoretical Debates to Interrogating and Transforming Praxis
Bibliographic record
Abstract
Abstract Cultural competence is one of the most debated concepts in social work. Whilst some scholars view it as a central tenet of social work theory, practice and education, others critique it as a new form of racism and even encourage its abandonment in social work. Amidst this fierce debate, this article attempts to answer the following question: How do we become entrapped by our own thinking when it comes to fostering or critiquing cultural competence and related constructs as well as understanding their embodiment in everyday social work practice? Using a post-structural lens, especially Foucault’s work on the ‘Order of Things’ in society, this article interrogates discourses and praxis in cross-cultural social work practice (CCSWP). After briefly examining the pervasive concepts of cultural competence, cultural humility and structural competence currently in circulation, this article elaborates upon debated discourses and interrogates the praxis underlying CCSWP. Instead of descending into semantic and theoretical debates and promoting one dogmatism above others, it is essential to assume a position of ongoing interrogation and critique of the pervasive discourses and praxis of cultural competence and alternative concepts. Doing so can promote reflective CCSWP and create the possibility for transformative change in social work practice.
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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 | 0.063 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.017 | 0.187 |
| Scholarly communication | 0.027 | 0.033 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 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".