Practicing alliance: an experiential model of teaching diversity and inclusion for social work practice and education
Bibliographic record
Abstract
Social work students are expected to engage diversity and difference in practice, yet few approaches in social work education explicitly focus on strengthening skills required for such allyship engagement. Constructs such as cultural competence, cultural humility, and intersectionality are often difficult for students to learn and for educators to teach effectively. This article describes Practicing Alliance, a curriculum that extends the Ally Model for Social Justice through a systematic integration of experiential learning theory, designed to increase the allyship skills of social work students across social locations. Participants (N = 85) in the pilot evaluation were students in the Foundation-year (n = 21) and second-year (n = 47) of a two-year MSW program, and 17 Advanced Standing MSW students. Practice experience ranged from 1–2 years (17.76%) to over 5 years (37.65%). Following completion of Practicing Alliance, 100% of participants reported that they were better able to practice allyship, 91% agreed that they were better equipped to intervene in incidents of discrimination, and 93% stated that Practicing Alliance contributed to their development as a social worker. Qualitatively, participants reported increased confidence in utilizing their allyship skills. The Practicing Alliance model is described with recommendations for implementation within 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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".