Sticker Shock and Looming Tsunami: The High Cost of Academic Serials in Perspective
Bibliographic record
Abstract
Recession is currently causing a resurgence of the academic serials crisis. Profit-mongering by commercial publishers is once again denounced as the key driver of the crisis. However, a critical analysis of institutional and bibliometric data does not reveal excessive corporate greed in recent years; instead, it suggests that the present hurdles stem largely from years of inadequate budget allocations to academic libraries and from a publishing frenzy fuelled by simplistic methods of evaluating faculty productivity. To prevent what is likely to be the publishing equivalent of a tsunami in the next few years, universities and research institutions urgently need to re-emphasize quality over quantity in the publishing process, and they must find ways to include peer-reviewing efficiency among their criteria for productivity and impact. Achieving these goals will require concerted efforts by researchers, librarians, and publishers.
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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 | 0.004 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.003 |
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".