How Journals and Publishers Can Help to Reform Research Assessment
Bibliographic record
Abstract
Journals and publishers recognize that editorial decisions can make or break researchers’ careers. It is well established that administrators and decision-makers use journal prestige and impact factors as a shortcut to assess the research of job applicants, current academic staff, and even proactively recruit academics who score highly on such metrics. It is not uncommon to find language in university evaluation policies that reference or explicitly mention the Journal Impact Factor (JIF). For example, a recent study found that the JIF or other closely related terms, including “high-impact journal” and “journal impact,” were mentioned in 23% of review, promotion, and tenure documents in a representative sample of academic institutions across the United States and Canada.1 This amount increased to 40% among research-intensive universities. However, such an approach to research evaluation provides a limited view of anyone’s accomplishments. Many groups also have argued that focusing on journal brands intensifies competition between researchers and journals in ways that distort behavior and undermine a healthy and productive scholarly enterprise.2,3 But it is not enough to recognize the problem. Identifying specific approaches that publishers can take to address these concerns really is key. The Declaration on Research Assessment (DORA)4 is doing that by advancing practical and robust approaches to improve how research is evaluated in hiring, promotion, and funding decisions. But change—which is essentially cultural—does not come easy. It hinges on the actions of individuals, organizations, and every stakeholder in the environment. When DORA was released in 2013, the declaration provided 18 targeted recommendations […]
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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 | 0.281 | 0.520 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.015 | 0.009 |
| Science and technology studies | 0.018 | 0.047 |
| Scholarly communication | 0.100 | 0.118 |
| Open science | 0.009 | 0.041 |
| Research integrity | 0.049 | 0.045 |
| Insufficient payload (model declined to judge) | 0.034 | 0.029 |
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".