‘Excellence’ in the Research Ecosystem: A Literature Review. RoRI Working Paper No. 5.
Bibliographic record
Abstract
The notion of ‘excellence’ has become an increasingly important part of the research ecosystem over the last 20 years and has shaped science policy, research funding and evaluation activities. Notions of excellence are mobilized in the context of national evaluation systems, institutional funding programs, grant project funding, Centers of Excellence, and play a role in individual career assessment. While omnipresent in the research ecosystem, there is no consensus on what ‘excellence’ means or how it should be recognized. This literature review analyses how notions of excellence have been understood in higher education and research systems, and how those understandings have evolved. It forms an initial output from a Research on Research Institute (RoRI) project, which is exploring how funders in the RoRI consortium use excellence in their work, and what strategies are being developed to broaden how the concept is defined and applied.
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 | 0.036 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.015 | 0.034 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".