How Journals and Publishers Can Help to Reform Research Assessment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 […]
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.
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 | 0.045 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it