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Quality Management in Higher Education: the Experience of Canada

2018· article· en· W2887702472 on OpenAlexaboutno aff
Faina Lazarevna Ratner, N.V. Tikhonova, Natal'ya Tihonova

Bibliographic record

VenueStandards and Monitoring in Education · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsnot available
Fundersnot available
KeywordsQuality (philosophy)Higher educationQuality assuranceBusinessProcess (computing)Coherence (philosophical gambling strategy)Public relationsPolitical scienceQuality managementMarketingComputer science

Abstract

fetched live from OpenAlex

The article examines the Canadian experience in the fi eld of education quality management, where the eff ectiveness of the whole system is provided by active cooperation of multiple actors of educational policy at all levels: international, national, regional and institutional. Of particular interest is the analysis of specifi c initiatives implemented at each level and ways to ensuring their coherence. A brief overview of quality assurance agencies monitoring and controlling the higher education sector in whole, the universities and the educational programs, is off ered. Despite the signifi cant heterogeneity of the university sector of Canada due to the administrative, territorial and cultural diff erences, the coordinated actions of all participants of the educational process contribute to cooperation between universities, ensure mutual recognition of diplomas both in Canada and internationally, and create conditions for students’ and graduates’ mobility.

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)0.005
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.995
Threshold uncertainty score0.889

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.011
Science and technology studies0.0310.008
Scholarly communication0.0110.002
Open science0.0030.006
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0050.000

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 head0.028
GPT teacher head0.395
Teacher spread0.367 · 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
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

Citations2
Published2018
Admission routes1
Has abstractyes

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