Keeping the Boss Happy: Black and Minority Ethnic Students’ Accounts of the Field Education Crisis
Bibliographic record
Abstract
Abstract Social work field education, the mandatory, practice-based component of accredited schools of social work, is in a state of crisis. Welfare state retrenchment has reduced the social and health service sectors’ capacity to provide field education placements. Concurrently, increasing student enrollment in and the expansion of social work programmes in the academy have increased the demand for field education. Whilst the service and academic sectors have developed a range of formal and informal relationships to cope with the crisis that often benefit workers in both domains, the implications for students, especially those who are Black and Minority Ethnic (BME), remain largely unknown. This article reports findings from institutional ethnographic research based on textual analyses and interviews with five BME students from a school of social work in Southern Ontario who were engaged in securing field education placement. A central finding of the study was that racial categories and hierarchies are reproduced across placement settings and in the sorting process of students into placement settings itself, adding to the work of BME social work students. The findings implicate the institutional practices and context of field education in the production of a racially stratified labour market in social work field education.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.032 | 0.019 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".