Library publishing workflows: Three big lessons learned from cohort-based documentation
Bibliographic record
Abstract
Over the past three decades, library publishing has moved from a niche activity to a regular part of many academic and research libraries’ services to their communities. Communities of practice have also grown up and matured around this work, including the Library Publishing Coalition. While the Library Publishing Forum, library publishing listservs, and other professional spaces are lively and active spaces for discussion, publishing workflows—depictions of all the functions performed by a library publisher as part of its regular operations—are generally undocumented. This makes cross-comparison across publishers difficult, leading to missed opportunities for peer learning and sharing of emerging good practices. It also makes it more challenging for individual publishers to evaluate their processes and identify crucial steps they may be omitting, such as contributing metadata to aggregators (essential for discovery and impact) and depositing content in preservation repositories (necessary for a stable scholarly record).
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 | 0.317 | 0.475 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.009 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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, 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".