Staffing of Library Publishing Programs in the United States and Canada: A Data-Driven Analysis
Bibliographic record
Abstract
Introduction: Using the Library Publishing Coalition’s (LPC) Research Dataset, this paper focuses on the staffing of library publishing programs at colleges, universities, and consortia in the United States and Canada from 2014 to 2022. Methods: In order to transform the data into a consistent format and write it into a single table as a commaseparated values (CSV) file, we created a program written in C# and executed on Windows 10. We narrowed the data set to focus on just library publishing programs from the United States and Canada, as well as to those that responded to the survey in early and later years. We also analyzed the data by enrollment and compared the staffing of library publishing programs to the staffing of academic libraries in general using the annual Association of College and Research Libraries (ACRL) Library Trends and Statistics Annual Survey data. Results: The average library publishing program relies largely on professional staff, has shown the most growth in paraprofessional staff, and has lost staff overall since 2019 while still showing growth overall since data collection began. Discussion: Compared with staffing of ACRL libraries in general, library publishing programs lost staff members at about a four-times higher rate from 2014 to 2021. Conclusion: From 2014 to 2022, the number of library publishing staff did not grow at the same rate as the number of staff in libraries did as a whole. Also, although there are certainly general conclusions or trends, there are also opportunities for additional quantitative and qualitative research to be done in this area. Link to code: https://github.com/jmeetz/LPCSurveyDataLoader Link to data: https://kb.osu.edu/handle/1811/104685
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.024 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| 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".