Connecting Research and Practice: Publication Patterns of LIS Faculty Who Teach Health-Related Courses
Bibliographic record
Abstract
Bibliometrics studies of library and information science (LIS) faculty scholarly output have explored publication patterns in open access (Grandbois & Beheshti, 2014), health-gender and sexual orientation (Mehra & Tidwell, 2014), and other trends in LIS Research (Wusu & Lazarus, 2018). This study builds upon that literature by exploring the publication characteristics of full-time LIS faculty teaching health courses and the scholarship patterns of this underexplored group. This bibliometric analysis examined the connections between research and practice by examining publications from 2011 to 2021 by LIS faculty that teach health-related courses for library science programs in the United States and Canada. The data sources were located through searching course listings, faculty profiles, and syllabi from school websites and contacting deans and directors to identify full-time LIS faculty teaching health-related courses in American Library Association (ALA) accredited programs. The 29 LIS faculty that were identified through this process were contacted via email in September 2021, inviting them to voluntarily share their curriculum vitae (CVs) for analysis. The final sample of 21 CVs is comprised of the 16 faculty members who responded to the email invitation providing their CVs and five CVs that were publicly available online. The research team used descriptive bibliometrics to explore author, author order, year of publication, source, type of publication, etc. Insight and implications pertaining to connecting LIS faculty research, teaching health-related courses, and practice will be presented, as well as recommendations for future research directions.
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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 | 0.113 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.033 | 0.064 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| 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".