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‘Excellence’ in the Research Ecosystem: A Literature Review. RoRI Working Paper No. 5.

2021· article· en· W3203023873 on OpenAlex

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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueFigshare · 2021
Typearticle
Languageen
FieldDecision Sciences
TopicResearch, Science, and Academia
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchAustrian Science FundFondazione TelethonSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungMichael Smith Health Research BCWellcome Trust
KeywordsExcellenceContext (archaeology)Political scienceEngineering ethicsPublic relationsSociologyEngineeringGeography

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.072
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.777
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.072
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.008
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0030.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.1200.022

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.

Opus teacher head0.391
GPT teacher head0.496
Teacher spread0.106 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it