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Record W2895191504 · doi:10.7202/1057105ar

Academic Drift in Canadian Institutions of Higher Education: Research Mandates, Strategy, and Culture

2019· article· en· W2895191504 on OpenAlexaffvenueabout
Lane Trotter, A. D. MITCHELL

Bibliographic record

VenueCanadian Journal of Higher Education · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsFanshawe CollegeLangara College
Fundersnot available
KeywordsHigher educationTransparency (behavior)Isomorphism (crystallography)InstitutionPublic administrationGovernment (linguistics)Context (archaeology)LegislationCorporate governancePolitical scienceAcademic communityInstitutional researchEconomic growthPublic relationsBusinessSociologyEconomicsSocial scienceFinanceGeography

Abstract

fetched live from OpenAlex

As with higher-education institutions around the world, British Columbia (BC) and Ontario are increasingly faced with demographic and market pressures that erode the traditional difference between the university and non-university sectors (i.e., colleges and institutes). Key components that ensure these provinces’ institutions preserve their unique roles and differentiations in a changing context, partially driven by their governments, include research mandates, transparency in institutional governance, and strategic documents that resist the academic drift created by institutional isomorphism. Both governments are actively reshaping their post-secondary systems to align with national or regional economic needs, increasing access, streamlining degree completion, and responding to community pressure to have a university or a degree-granting institution. An analysis of the enabling legislation, government policy directives, and institutional documents of both provinces shows that there is a blurring in the distinction between colleges and universities, and the costs associated with this.

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)0.053
metaresearch head score (Gemma)0.049
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.947
Threshold uncertainty score0.763

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0530.049
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.013
Science and technology studies0.0360.025
Scholarly communication0.0170.003
Open science0.0040.008
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.051
GPT teacher head0.408
Teacher spread0.357 · 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
DomainIncentives
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

Citations5
Published2019
Admission routes3
Has abstractyes

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