Turning the tables: A university league-table based on quality not quantity
Bibliographic record
Abstract
<ns4:p> <ns4:bold>Background:</ns4:bold> Universities closely watch international league tables because these tables influence governments, donors and students. Achieving a high ranking in a table, or an annual rise in ranking, allows universities to promote their achievements using an externally validated measure. However, league tables predominantly reward measures of research output, such as publications and citations, and may therefore be promoting poor research practices by encouraging the “publish or perish” mentality. </ns4:p> <ns4:p> <ns4:bold>Methods:</ns4:bold> We examined whether a league table could be created based on good research practice. We rewarded researchers who cited a reporting guideline, which help researchers report their research completely, accurately and transparently, and were created to reduce the waste of poorly described research. We used the EQUATOR guidelines, which means our tables are mostly relevant to health and medical research. </ns4:p> <ns4:p> <ns4:bold>Results:</ns4:bold> Our cross-sectional tables for the years 2016 and 2017 included 14,408 papers with 47,876 author affiliations. We ranked universities and included a bootstrap measure of uncertainty. We clustered universities in five similar groups in an effort to avoid over-interpreting small differences in ranks. </ns4:p> <ns4:p> <ns4:bold>Conclusions:</ns4:bold> We believe there is merit in considering more socially responsible criteria for ranking universities, and this could encourage better research practice internationally if such tables become as valued as the current quantity-focused tables. </ns4:p>
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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 | 0.064 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.009 |
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".