Bibliographic record
Abstract
In addition to the growing number of scholarly journals published by the so-called “big five”, there are tens of thousands of journals that are published by individual scholars or by academic institutions. These smaller operations are a source of great bibliodiversity that deserves to be encouraged but can also be seen as inefficiencies in the system as a whole. The use of a common software—Open Journal Systems (OJS)—is helping these journals take advantage of an economy of scale without needing to centralize or homogenize them. The key to promoting both efficiency and bibliodiversity is in OJS’s open source nature. This presentation will describe the ways in which PKP’s open source software is bringing efficacy to journal operations, to the discovery of their content, and, in the best of cases, to supporting a transformation of the system as a whole.
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 | 0.066 | 0.234 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.047 | 0.064 |
| Open science | 0.008 | 0.038 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.046 | 0.029 |
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".