Bibliodiversity of Small Academic Publishers
Bibliographic record
Abstract
Large bibliographic databases highlight tangible and symbolic differences regarding the standards of quality attached to them, underlining diverging incentive structures for small and large academic publishers. To assess the academic differences associated with these, we explore bibliometric data for small publishers' journals from the Web of Science (WoS) and Scopus. We then discuss the visibility and impact of highly cited literature in small open access journals in relation to their cited references from indexed and non-indexed sources. We find that non-indexed references are consistently relevant for highly cited literature, yet the share of items that obtains high citation counts is rather small and uneven across disciplines. In general, we identify regional and linguistic specificities, whilst there are some observable thematic differences compared to more mainstream publications. In particular, we underline that a healthy bibliodiversity can, dependent on language or regional contexts, shape epistemic and scientific practices and narratives.
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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 | 0.016 | 0.134 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.012 | 0.020 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.024 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".