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Record W2790515660 · doi:10.1177/0950422218761500

Theory, practice and policy

2018· article· en· W2790515660 on OpenAlex
Qiantao Zhang

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIndustry and Higher Education · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsUniversity of Toronto
FundersScottish Funding Council
KeywordsBoosting (machine learning)Higher educationEconomicsPolicy analysisPolitical scienceRegional sciencePublic economicsBusinessAccountingPublic administrationSociologyEconomic growth

Abstract

fetched live from OpenAlex

This article examines the progress of university knowledge exchange in the United Kingdom over a decade, linking theory, practice and policy. As indicated by the literature, the performance of university knowledge exchange is influenced by institutional and locational characteristics. Data on 133 UK universities between 2003–2004 and 2012–2013 are used in the empirical analysis, which confirms the important roles of institutional (established vs. new) and locational (competitive vs. uncompetitive) characteristics of universities. Nevertheless, it is found that the current policy approach in the United Kingdom fails to consider the heterogeneity of the higher education sector. This is problematic given that a substantial amount of funding has been committed to boosting university knowledge exchange over the past 20 years. In addition, the results suggest that the knowledge exchange policy should not be implemented alone; rather, it should be developed in conjunction with industrial and innovation policies.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.857
Threshold uncertainty score0.743

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.026
GPT teacher head0.390
Teacher spread0.364 · 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