Bibliographic record
Abstract
Purpose – This paper aims to examine the attractiveness of Library and Information Science (LIS) professions and programs to culturally and linguistically diverse individuals. Design/methodology/approach – Between September and December 2014, current students and alumni from 57 North American LIS programs were surveyed regarding their learning experiences and perceptions of the state of diversity in LIS. Findings – The findings point to deep, emotive reflections on diversity in LIS. Noting the general societal turn toward values-based, integral diversity, this paper proposes looking beyond the quantitative measures and paying attention to the volume of negative emotion surrounding the diversity debate in our field. Making both philosophical and practical arguments, a three-tiered approach is advocated, which can contribute to nurturing the climate of diversity: outreach and promotion; recruitment and retention; and interpersonal and intercultural dialog that will not only sustain diversity but also transform diverse environments into healthy and vibrant places with transparent communication channels. Originality/value – This paper departs from the focus on increasing diversity and emphasizes sustaining diversity in both academia and workplaces. The improvement of interpersonal relationships, human understanding and interpersonal communication is seen as a way to systemic change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.022 |
| Scholarly communication | 0.019 | 0.023 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.057 | 0.036 |
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".