MétaCan
Menu
Back to cohort
Record W3049184741

A Comparative Analysis of the K-12 International Education Policies of Ontario and Manitoba.

2020· article· en· W3049184741 on OpenAlexaffvenueabout
Roopa Desai Trilokekar, Merli Tamtik

Bibliographic record

VenueCanadian Journal of Educational Administration and Policy · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsUniversity of ManitobaYork University
Fundersnot available
KeywordsPolicy analysisContext (archaeology)Equity (law)StakeholderGovernment (linguistics)Political sciencePublic administrationEducation policyPublic policyInternational educationContent analysisPublic relationsSociologyHigher educationSocial scienceGeography
DOInot available

Abstract

fetched live from OpenAlex

This paper utilizes critical policy analysis framework to examine provincial policy documents in K-12 international education. Adopting Ball’s (1994) three policy contexts framework – 1) the context of influence; 2) the context of policy text production; and 3) the context of practice - this paper provides a comparative analysis of the international education policies of Ontario and Manitoba. The paper shows that policy documents are not simply linear government directives but they are rather processes driven by local stakeholders including schools, school boards, non-government organizations and educational administrators.Through this comparison, informed by document analysis and stakeholder interviews, we provide an understanding of what factors have led to K-12 international education impetus, and discuss the potential outcomes in terms of (under) privileging of certain values, its effects on (in) equity, and its long-term implications for a publically funded educational system in Canada.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.192
Threshold uncertainty score0.938

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.009
Science and technology studies0.0090.002
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.060
GPT teacher head0.382
Teacher spread0.321 · 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 designQualitative
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

Citations7
Published2020
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Educational Administration and PolicySame topicGlobal Education and MulticulturalismFrench-language works237,207