MétaCan
Menu
Back to cohort
Record W4290786367 · doi:10.5206/cieeci.v50i2.13982

We just try to work with the needs in front of us

2022· article· en· W4290786367 on OpenAlexaffvenueabout
Nancy Bell, Roopa Desai-Trilokekar

Bibliographic record

VenueComparative and International Education · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsYork University
Fundersnot available
KeywordsEnthusiasmEthosWork (physics)Government (linguistics)Public relationsPolitical scienceFocus groupProfessional developmentPedagogySociologyPsychologyEngineering

Abstract

fetched live from OpenAlex

The Ontario government’s International Education Strategy (2015) establishes goals for international education and the hosting of international students in Ontario schools. Using a critical policy lens, we focus on teachers as key to enacting these policy objectives in local contexts. The article presents findings from four focus groups of Ontario teachers working in schools with large international student cohorts. We found teacher participants were generally unaware of a broader vision or strategic goal for the presence of these students in their schools. They found it difficult to meet the needs of this growing cohort, and they faced this challenge with varying degrees of enthusiasm. Factors that influenced their enactment included personal background and education, as well their ability to collaborate and work in contexts with a professional ethos. Overall, teachers expressed a desire for more information, support, and professional development, including opportunities for collaboration, with regard to educating international students in their schools.

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)0.006
metaresearch head score (Gemma)0.015
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: none
Teacher disagreement score0.297
Threshold uncertainty score0.591

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0290.029
Scholarly communication0.0130.021
Open science0.0020.014
Research integrity0.0100.022
Insufficient payload (model declined to judge)0.0260.011

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 head0.095
GPT teacher head0.417
Teacher spread0.322 · 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

Citations2
Published2022
Admission routes3
Has abstractyes

Explore more

Same venueComparative and International EducationSame topicGlobal Education and MulticulturalismFrench-language works237,207