MétaCan
Menu
Back to cohort

Canadian Higher Education System: Evaluation of the HEQCO Agency for the development of Education in the province of Ontario

2022· article· en· W4282591906 on OpenAlexaboutno aff
Danilo de Melo Costa

Bibliographic record

VenueEnsaio Avaliação e Políticas Públicas em Educação · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsnot available
Fundersnot available
KeywordsAgency (philosophy)AutonomyGovernment (linguistics)Exploratory researchHigher educationAssertivenessPolitical sciencePublic relationsQuality (philosophy)Order (exchange)Public administrationSociologyBusinessPsychologySocial scienceSocial psychology

Abstract

fetched live from OpenAlex

Abstract Canada is a country with a recognized Education system, and the province of Ontario has the largest number of students enrolled in Higher Education. Due to its management complexity, the Government of Ontario created the agency Higher Education Quality Council of Ontario (HEQCO). Because it is an unusual agency in most countries, this research aims to better understand HEQCO under the vision of the main stakeholders of the system. In order to reach the results, exploratory and qualitative research was developed, from the accomplishment of interviews with managers of the Canadian government and application of questionnaire for six professors specialists in Canadian Higher Education and two student leaderships. The results showed that HEQCO has a prominent role but needs greater autonomy. This study also realized that in highly centralized systems, having an agency acting on specific issues can be an efficient way to identify the greatest challenges and be more assertive in the actions and policies outlined for each locality, assisting in its development.

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)0.024
metaresearch head score (Gemma)0.045
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.801
Threshold uncertainty score0.929

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.045
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.008
Science and technology studies0.0120.003
Scholarly communication0.0070.002
Open science0.0020.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.049
GPT teacher head0.357
Teacher spread0.308 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueEnsaio Avaliação e Políticas Públicas em EducaçãoSame topicHigher Education Governance and DevelopmentFrench-language works237,207