Teaching Diversity, Becoming Inclusive: Perspectives and Possibilities in ASEAN Library and Information Science Schools
Bibliographic record
Abstract
This paper investigates how courses and topics about diversity and inclusion are being integrated into the Library and Information Science (LIS) curriculum among selected library schools in the member countries of the Association of Southeast Asian Nations (ASEAN). It also examines the characteristics and roles of culturally responsive LIS faculty in teaching cultural diversity-related issues. In addition, it discusses the approaches, challenges and barriers in advancing cultural competencies of LIS graduates. This study used a mix of qualitative survey research and content analysis of LIS course offerings. Findings show that ASEAN LIS education is inadequate in terms of integration of diversity and inclusion in core courses. Teaching cultural competence and diversity is a lifelong learning process of understanding and appreciating cultural similarities and differences. LIS students may develop their cultural competence through personal experience by engaging in library internship with all library types, community immersion and collaborative group work with diverse members. Infusing diversity-related issues and the principle of inclusion into the LIS curriculum is not easy, yet important and necessary.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.025 | 0.008 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".