Bibliographic record
Abstract
David Henige 1 has blown dust off of an old and indigestible chestnut in the manner of a morality play. Grossly overpriced journals are common in many fields, yet they are also usually successful in attaining a wide circulation (mainly to libraries), which seems illogical. Although Henige casts doubt on at least some of the criteria that librarians employ to determine their ‘retained list,’ there is little doubt that they usually ‘know their constituencies [of faculty and students] and collect the most in-demand items first.’ 2 It is the demand for these journals by the ‘constituencies’ that really promotes their success (or otherwise). My experience in my own field, palaeontology, is that many journals provide a similar product, but that publishinghouse journals explore particular facets of the speciality, making them essential reading to a (commonly large) subset of the potential audience. Although Henige concentrates on the aspect of value for money, many criteria can be identified to support the continuation of subscriptions to expensive journals on the library budget, although I accept that cost is usually the most important. Among these criteria, I propose that two factors particularly act to maintain the status quo, one intrinsic – the high profile such journals have among researchers – and the other extrinsic – the resistance (one might say apathy, in some cases) of the ‘constituencies’ to change.
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 | 0.021 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.016 | 0.023 |
| Open science | 0.009 | 0.008 |
| Research integrity | 0.070 | 0.056 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".