Teaching and learning in adult and higher education, the example of anti-racism and anti-oppression training for social work field instructors
Bibliographic record
Abstract
The purpose of this study was to explore which content and teaching methods best facilitate field instructors' learning about anti-racism and anti-oppression issues and assist them in teaching students the skills to incorporate these issues in their work. Coupled with examining issues related to the content, format, impact and organizational context of training, determining future training needs of field instructors was another focus of this study. Based on the purpose of the research and my own learning goals, a qualitative research approach was utilized in this study. In-person and telephone interviews were conducted with field instructors affiliated with the two Canadian universities. The findings indicate that social work field instructors prefer a participatory approach in their learning at anti-racism and anti-oppression training events, as well as in their own field teaching. Training provides field instructors with opportunities to build new knowledge about the issues, affirms their current knowledge and efforts in this area and offers them creative strategies to take action regarding anti-racism and anti-oppression. The agency climate was found to impact the nature and degree of anti-racist and anti-oppressive field education. Field instructors also provided numerous insights for future training events. Recommendations for future training events include mandatory anti-oppression and anti-racism training for new and veteran field instructors; opportunities for on-going networking and dialogue between stakeholders; the development and dissemination of written resources; the enhancement of social work curricula to include greater emphasis on the issues; and the promotion of additional research in this area of social work 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
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".