Publications Produced and Services Offered by 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 type and number of publications as well as services offered by 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 CSV file, we created a program written in C# and executed it on Windows 10. We narrowed the dataset to focus on just library publishing programs from the United States and Canada, and those that responded to the survey in early and later years. We also analyzed the data by enrollment and used the findings from our previous paper on staffing of library publishing programs to add context. Results: From 2014 to 2022, the average library publishing program published mostly open access facultycreated journals, about three textbooks per year, and less than one monograph per year. On average, fewer journals were published in 2022 than in 2014. In 2022, the average library publishing program offers about one more service than it did in 2014. Discussion: The average number of publications and services both peaked in 2020, while the average number of staff peaked in 2019. As of 2022, staff, services, and the number of journals published have not rebounded since their respective peaks. Conclusion: From 2014 to 2022, the number of journals and monographs published by the average library publishing program decreased, while the number of textbooks published and services offered increased. Also, though there are certainly general conclusions or trends, there are also opportunities for additional quantitative and qualitative research to be done in this area.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Scholarly communication Domain: not available · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: yes | Observational | high |
| gpt | Scholarly communicationOpen science Domain: not available · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: yes | Observational | high |
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.034 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".