A Narrative Inquiry-Informed Exploration of Practices Meant to Enhance Diversity Literacy: Challenges and Recommendations for Social Work
Bibliographic record
Abstract
Effective strategies for navigating diversity in educational and direct practice settings are essential. Given rapid diversification in North America, scholars have called for qualitative research on diversity practices. In response, this study answers the question: What, if any, contemporary diversity-centered practices limit or impede the development of diversity literacy, the attitudes, knowledge, and skills necessary to interact with populations respectfully and sensitively? Narrative-informed inquiry was used to explore perceptions among 32 social work students and alumni from one university in Ontario, Canada. Analysis produced six themes, which suggest that diversity literacy may be constrained by: (1) categorizing people into boxes, (2) discussing social identities too simplistically, (3) conflating diversity discussions and social justice efforts, (4) priming differences in a manner that makes them inordinately salient, (5) endorsing, rather than challenging, assumptions and stereotypes about social identity groups, and (6) limiting difficult conversations about differences. The findings position social work students and professionals with the knowledge to foster more culturally attuned interactions with populations that differ from their own in educational and direct practice settings. Future research might employ intersectional samples of social workers across North America, using both qualitative and quantitative designs, to study the effectiveness of diversity-centered practices.
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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 | 0.105 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.020 | 0.033 |
| Scholarly communication | 0.032 | 0.046 |
| Open science | 0.007 | 0.023 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".