Do Languages Represent?: A Pilot Study on Linguistic Diversity and Library Staff
Bibliographic record
Abstract
This paper aims to add to conversations on access, diversity, and representation in libraries by addressing the importance of language as a factor in library service and providing some of the first data on library workers’ language skills. Much of the literature on language in libraries focuses on issues of multilingual access and collection development, and there is less emphasis on the roles of staff and language skills in providing and mediating access. As well, while US librarians are less diverse than US library workers and the wider population in terms of gender and race, it has not been shown whether the same holds true in terms of language.\nA pilot study of staff from three US public libraries sought to address these gaps in knowledge about staff language skills and representation and to generate further lines of inquiry. Responses were compared with US Census data to determine linguistic representation relative to the service population. The results indicated that while staff surveyed were more likely than the wider population to know another language besides English, they were not likely to use that language on the job, and those who did use a language besides English often reported low fluency. Responses also showed differences in language knowledge and use between staff with and without MLIS degrees. The results highlight the differences between language knowledge, fluency, and usage, offer implications for library service and professional values, and suggest many future directions for research.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".