Library Publishing Services: Strategies for Success Research Report Version 1.0
Bibliographic record
Abstract
Over the past five years, libraries have begun to expand their role in the scholarly publishing value chain by offering a greater range of pre-publication and editorial support services. Given the rapid evolution of these services, there is a clear community need for practical guidance concerning the challenges and opportunities facing library-based publishing programs.Recognizing that library publishing services represent one part of a complex ecology of scholarly communication, Purdue University Libraries, in collaboration with the Libraries of Georgia Institute of Technology and the University of Utah, secured an IMLS National Leadership Grant under the title “Library Publishing Services: Strategies for Success.” The project, conducted between October 2010 and September 2011, seeks to advance the professionalism of library-based publishing by identifying successful library publishing strategies and services, highlighting best practices, and recommending priorities for building capacity.The project has four components: 1) a survey of librarians designed to provide an overview of current practice for library publishing programs (led by consultant October Ivins); 2) a report presenting best practice case studies of the publishing programs at the partner institutions (written by consultant Raym Crow); 3) a series of workshops held at each participating institution to present and discuss the findings of the survey and case studies; and 4) a review of the existing literature on library publishing services. The results of these research threads are pulled together in this project white paper.
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 | 0.044 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.028 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.034 | 0.024 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.090 | 0.051 |
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".