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Record W2054174565 · doi:10.14507/epaa.v8n30.2000

Performance Models in Higher Education

2000· article· en· W2054174565 on OpenAlexaff
Janet Atkinson‐Grosjean, Garnet Grosjean

Bibliographic record

VenueEducation Policy Analysis Archives · 2000
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsGovernmentalityAccountabilityIdeologySociologyContext (archaeology)State (computer science)Higher educationPublic administrationField (mathematics)Set (abstract data type)Public relationsPolitical sciencePoliticsLaw

Abstract

fetched live from OpenAlex

Higher education (HE) administrators worldwide are responding to performance-based state agendas for public institutions. Largely ideologically-driven, this international fixation on performance is also advanced by the operation of isomorphic forces within HE's institutional field. Despite broad agreements on the validity of performance goals, there is no "one best" model or predictable set of consequences. Context matters. Responses are conditioned by each nation's historical and cultural institutional legacy. To derive a generalized set of consequences, issues, and impacts, we used a comparative international format to examine the way performance models are applied in the United States, England, Australia, New Zealand, Sweden, and the Netherlands. Our theoretical framework draws on understandings of performance measures as normalizing instruments of governmentality in the "evaluative state," supplemented by field theory of organizations. Our conclusion supports Gerard Delanty's contention, that universities need to redefine accountability in a way that repositions them at the heart of their social and civic communities.

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 imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.989
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.005
Science and technology studies0.0020.012
Scholarly communication0.0090.009
Open science0.0020.003
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.183
GPT teacher head0.497
Teacher spread0.315 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designTheoretical or conceptual
DomainEvaluation
GenreEmpirical

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".

Quick stats

Citations8
Published2000
Admission routes1
Has abstractyes

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