Bibliographic record
Abstract
We assess the importance of publisher conduct and journal quality in determining the price and cost-effectiveness of journals in geography. Drawing on a database of 136 journals in which geographers publish, we examine the price of journals, publishers' market share, the determinants of journal prices, and the cost-effectiveness of journals. We find that commercial presses charge 2.3 times more for journals than society and university presses and 50 percent more for journals published on behalf of universities and societies. Journals in physical geography are almost twice as expensive as those in human geography and 43 percent of them can be considered “overpriced.” Four commercial presses hold 72 percent of the titles and 93 percent of the value of subscriptions, enabling them to exercise oligopolistic market power over pricing. Our multivariate analysis shows that the price of journals, of articles, and of citations in geography is driven by publisher conduct rather than journal quality as measured by citations accrued to articles. Geographers can further the transition underway in scientific publishing by self-archiving their published work, disseminating their findings through social networks, and paying closer attention to journal cost-effectiveness in choosing where to publish and review.
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 | 0.007 | 0.131 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".