Scholar-Librarian Collaboration in the Publication of Scholarly Materials
Bibliographic record
Abstract
Recent developments in scholarly publication and the new directions being pursued in both humanities departments and libraries in the production of digital content provide opportunity for scholars and libraries to explore new models for working together to produce and disseminate scholarly materials. We offer as a first step toward a model for publication the case of Opuscula: Short Texts of the Middle Ages and Renaissance (OSTMAR), a hybrid form of publication that leverages the university library infrastructure to create a platform for the publication of scholarly primary materials, an area of publication formerly reserved for the commercial press. This model is dependent on close collaboration between scholar and librarian, the nuances of which are outlined in this 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 | 0.055 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.018 |
| Science and technology studies | 0.016 | 0.016 |
| Scholarly communication | 0.047 | 0.037 |
| Open science | 0.003 | 0.030 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.010 |
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".