Applied Methods of Teaching about Oppression and Diversity to Graduate Social Work Students: A Case Example of Digital Stories
Bibliographic record
Abstract
Social work education accreditation requires the completion of course work specifically around issues related to oppression and diversity within society. Educators offer a range of approaches to engage students in discussion about oppression and diversity from academic content and structured curriculum to reflective practice and experiential learning opportunities. The following describes a Master of Social Work course on oppression, social justice, and diversity offered at a western Canadian university that utilized a mixed method of teaching practices – including the creation of digital stories by the students in small groups. Beyond this description of the course content, students’ insight into the impact of using digital stories for their own learning and application to professional practice around issues of oppression and diversity is presented and discussed. Pour être agréé comme travailleur social, il faut avoir suivi des cours axés sur les questions d’oppression et de diversité au sein de la société. Pour faire participer les étudiants, les éducateurs emploient diverses méthodes qui vont des discussions à partir d’un contenu universitaire à un programme d’enseignement structuré et utilisent une approche réflective ainsi que des occasions d’apprentissage expérientiel. L’article traite d’un cours de maîtrise en travail social sur l’oppression, la justice sociale et la diversité, offert dans une université de l’Ouest du Canada, qui utilise une méthode combinée de pratiques d’enseignement, y compris la création d’histoires digitales par les étudiants en petits groupes. En plus d’en décrire le contenu, l’article présente un aperçu de l’impact de l’utilisation de ces histoires sur l’apprentissage des étudiants et de son application aux pratiques professionnelles entourant les questions d’oppression et de diversité.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.022 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".