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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.070 | 0.264 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.046 | 0.065 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.197 | 0.105 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".