Bibliographic record
Abstract
OBJECTIVE: The Impact Factor (IF) has received virtually no attention in the psychiat ric literature, despite its long-term use, expanding influence and evidence of misapplication. We examine the IF's validity as a measure of a paper's scientific worth, and consider alternative ways to conduct such an appraisal. METHOD: We explored medical databases and websites, and conferred with acknowledged experts on the subject. RESULTS: Irremediable problems, both conceptual and technical, make the IF a flawed measure. The notion that citations vouch for the quality of an article is questionable. Moreover, the IF's vulnerability to misuse in domains such as academic promotion and research grant assessment is a serious development. CONCLUSION: The IF (and all measures derived from it) should be abandoned. A "return to basics" in evaluating published work is overdue. As seductive as a simple formula is to assess quality, shortcuts are unavailable and unlikely to be useful. Publishing a short-list of papers annually, judged as objectively as possible by peers to merit special attention, may be a more meaningful option. Conceivably, every psychiatric journal could participate in this cyclical exercise, leading to a "grand short-list". This could be made readily available to all professionals, both researchers and clinicians, by being posted on a suitable website. Since peer review has a long-standing role in scientific publishing, our proposal is essentially an extension of that process.
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 | 0.190 | 0.520 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.053 | 0.011 |
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; the direct Gemma label and the distilled Codex classifier 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".