Bibliographic record
Abstract
Over the past decade, the field of Library and Information Studies (LIS) has promoted cultural competence as a means of improving services to multicultural or traditionally underserved populations. However, critical LIS scholars have noted that cultural competence and diversity are viewed predominately as a matter of skill acquisition on the part of the library worker, rather than as one that involves a critique of the forces of structural racism, discrimination, and colonialism in society. In response, this paper proposes adapting frameworks from other professions for the library context: Multi-Dimensional Cultural Competence (MDCC) from counselling psychology, and cultural safety (CS) from Indigenous nursing. The former views cultural competence in terms of diversity factors, components, and multiple levels of foci, while the second is premised in postcolonial understandings and respect for Indigenous knowledges. The proposed synthesis, Multi-Dimensional Cultural Safety (MDCS), is established by first identifying the epistemological and ideological shortcomings of cultural competence, focusing on the need for institutional critiques as well as a recognition of racialization and power structures. Next, the two existing frameworks are explained in terms of their origins, content, and professional contexts, noting how each addresses the shortcomings of cultural competence as well as each other’s shortcomings. The proposed framework is then elaborated upon in an LIS context and illustrated with hypothetical examples.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.022 | 0.046 |
| Scholarly communication | 0.022 | 0.011 |
| Open science | 0.002 | 0.026 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".