How Far Apart Are L and M? The Institutional and Publishing Disconnects between LIS and Museum Studies
Bibliographic record
Abstract
This article explores two considerations in the push toward joint “LAM” (Library, Archive, and Museum) programs of education and research: the organizational proximity of departments and schools of library and information studies (LIS) and museum studies (MS); and the degree to which individual scholars of LIS and MS share publishing outlets, as an indicator of current levels of scholarly interaction. An environmental scan of LIS and MS programs in the United States, Canada, the United Kingdom, Australia, and New Zealand was conducted to investigate the extent to which the two sets of programs were based in different universities and disciplinary units. A bibliometric survey was also carried out to gauge the extent to which LIS and MS scholars based in Australia publish in common journals, conference proceedings, and books. Findings show that the extent to which LIS and MS programs are offered by the same universities and colleges varies widely across countries, even within the English-speaking world. Further, the results suggest that while museum and curatorial studies tend to be located with arts and humanities disciplines, LIS programs are more likely to be located, particularly in North America, with the social sciences and ICT, although the disciplinary location of LIS programs is relatively diffuse. The bibliometric analysis confirmed the authors’ hypothesis that Australian LIS and MS academics publish in different outlets, with academics from the two groups presenting at only one conference in common and publishing in no common journal in the period studied.
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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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.010 | 0.028 |
| Scholarly communication | 0.024 | 0.019 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| 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".