Electronic Journals, Prestige, and the Economics of Academic Journal Publishing
Bibliographic record
Abstract
In their article "Electronic Journals, Prestige, and the Economics of Academic Journal Publishing" Steven Tötösy de Zepetnek and Joshua Jia discuss the current state of the academic journal publishing industry. The current state of the industry is an oligopoly based on a double appropriation model where academics produce work for at no cost only to have publishers earn significant profit margins by selling the work back to academics. Publishers are able to do this given the price inelasticity and weak bargaining power of its main consumer, university libraries. Publishers' ability to increase prices is also supported by what the authors term as the "prestige multiplier effect" and the "prestige crowd-out effect" which means the tendency for libraries to cut small publishers as large publishers raise prices because large publishers are more prestigious. To date, the usage of electronic journals has not changed this general model. Tötösy de Zepetnek and Jia argue that in order to progress towards a more equitable model of knowledge management allowing for the dissemination of knowledge globally and against the "colonialism of knowledge" a change in attitude and practices is required not only by publishers, but also by academics. Once perception changes and electronic journals obtain prestige, the publishing of scholarship electronically will replace or will be at least parallel to the prestige of print journals.
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".