Bibliographic record
Abstract
Background: In the face of extensive, developed-world library endorsement of open access (OA) and not-for-profit publishing, large commercial journal publishers are, paradoxically, increasing market share by means of economies of scale brought about in part by ownership concentration.Analysis: While the market success of commercial journal publishers may benefit from ownership concentration, it is argued that market-oriented innovation has also contributed to their market success. A review of the very lively state of market-oriented innovation in journal publishing and usage metrics is undertaken and three innovation proposals derived from commercial magazines are introduced.Conclusion and implications: The adoption of reader-focused features of commercial journals and the adaptation of the mobile-oriented strategy of commercial magazine publishers that respond to the modern digital information environment and mindset are recommended as strategically sound. Partnering with low-cost promoting, OA-oriented libraries may hobble the ability of not-for-profit journals to maximize their value to researchers.Originality/value: The weakness of OA as a constraining publishing strategy is brought forward and compared to readership building through innovation focused on usage.
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 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.004 | 0.029 |
| Scholarly communication | 0.025 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".