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Record W4388146429 · doi:10.24124/c677/20211623

Tuition Policy Instruments in Canada Public Policy Choices for What Problems

2022· article· fr· W4388146429 on OpenAlexaffvenueabout
Deanna Rexe

Bibliographic record

VenueCanadian Political Science Review · 2022
Typearticle
Languagefr
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsTypologyPolitical sciencePublic policyPublic administrationHumanitiesSociology

Abstract

fetched live from OpenAlex

AbstractUsing policy instruments as the unit of analysis and employing an instrument typology, this study considers policymaker goals and the effects of different policy actors and their influence strategies on selection. This study builds upon conceptions of choice approaches to policy instruments, using an analytical lens to describe policy actor perceptions and policy instrument use in three Canadian provinces to shed new light on the nature of higher education policy design.RésuméEn utilisant les instruments de politique comme unité d'analyse et en employant une typologie d'instruments, cette étude considère les objectifs des décideurs politiques et les effets des différents acteurs politiques et leurs stratégies d'influence sur la sélection. S’appuyant sur les conceptions des approches de choix des instruments de politique et utilisant une lentille analytique pour décrire les perceptions des acteurs politiques ainsi que les instruments de politique dans trois provinces canadiennes, cette étude servira de tremplin afin de jeter un nouveau regard sur la nature de la conception des politiques d'enseignement supérieur.Key Words: policy instruments; higher education; provincial policy; tuition policyMots-clés : instruments de politique; enseignement supérieur; politique provinciale; politique de scolarité

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)0.012
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.948
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.010
Science and technology studies0.0010.001
Scholarly communication0.0010.003
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.190
GPT teacher head0.471
Teacher spread0.281 · 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.

Study designNot applicable
Domainnot available
GenreCommentary

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 routes3
Has abstractyes

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