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Record W2612197653 · doi:10.7202/1038832ar

Accountability Agreements for Ontario Universities: The Balancing Character of a Policy Instrument

2017· article· en· W2612197653 on OpenAlexvenueaboutno aff
Victoria E. Díaz

Bibliographic record

VenueRevue Gouvernance · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsnot available
Fundersnot available
KeywordsAccountabilityNegotiationAutonomyGovernment (linguistics)Rhetorical questionControl (management)Independence (probability theory)Value (mathematics)Public relationsPublic administrationBusinessCharacter (mathematics)Compliance (psychology)Political scienceEconomicsManagementPsychologyComputer scienceSocial psychologyLaw

Abstract

fetched live from OpenAlex

This paper demonstrates how the choice of instrument facilitates acceptance of a new accountability requirement in the Ontario university sector as it helps balance the government’s need for control with the universities’ need for independence. The instrument, conceptualized as an agreement, embodies the negotiated character of the relationship between government and universities, and conveys the idea to different actors that their needs are met. Despite the promises of the instrument, when objectives are ambiguous, uncertainty is pervasive, and negotiation is limited, the increase in government control is minimized and the changes in university autonomy are negligible, thus suggesting that symbolic and rhetorical compliance may be the sustainable equilibrium between governments and governed. Nonetheless, some level of transformation is observed in the sector as the new tool contributes to strengthening priority alignment, highlighting the value of sharing stories, and increasing acceptance of reporting requirements.

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)0.035
metaresearch head score (Gemma)0.053
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.977
Threshold uncertainty score0.935

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0350.053
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0230.021
Scholarly communication0.0160.006
Open science0.0020.009
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.030
GPT teacher head0.327
Teacher spread0.296 · 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 designQualitative
Domainnot available
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

Citations1
Published2017
Admission routes2
Has abstractyes

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