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Record W2981341004 · doi:10.1080/23322969.2019.1679662

Student pathways and differentiation policies in Ontario: are they compatible?

2019· article· en· W2981341004 on OpenAlexaffabout
Stacey Young, Pierre Gilles Piché, Glen A. Jones

Bibliographic record

VenuePolicy Reviews in Higher Education · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsInstitute for Christian StudiesUniversity of Toronto
Fundersnot available
KeywordsPublic policyPublic relationsPolitical scienceDiversity (politics)Policy developmentPolicy studiesPolicy analysisPublic economicsPublic administrationEconomics

Abstract

fetched live from OpenAlex

Over the last 15 years, the government of Ontario, Canada began seeking ways to deliver and expand higher education in a more cost effective and sustainable manner through the introduction of two major policy goals: greater institutional differentiation and the expansion of student pathways. This paper will attempt to determine the compatibility of these two policy goals through a review of the relevant literature to determine if the policies are aligned from an efficiency and effectiveness, and public policy perspective. It will also identify a number of policy levers used in Ontario that may affect the extent of diversity and student pathways through document analysis, to assess their compatibility by making a limited use of the field of organisational theory as a lens to place the policies into context. It will also examine the extent to which various institutional types in Ontario have been engaged in student mobility and will compare and contrast the various strategies used to satisfy these public policy goals through textual analysis to highlight current successful institutional strategies that can be used by other jurisdictions. It will conclude with some key observations that the authors feel are necessary for either policy goal to succeed.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.248
Threshold uncertainty score0.685

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.068
GPT teacher head0.374
Teacher spread0.306 · 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 teacher head, 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

Citations2
Published2019
Admission routes2
Has abstractyes

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