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Record W4309084601 · doi:10.5206/cieeci.v51i1.15481

Introduction to Special Issue

2022· article· en· W4309084601 on OpenAlexaffvenueabout
Ann H. Kim, Elizabeth Buckner, Jean Michel Montsion

Bibliographic record

VenueComparative and International Education · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsUniversity of TorontoYork University
Fundersnot available
KeywordsIndigenizationInternationalizationEquity (law)Political scienceChinaInclusion (mineral)SociologyPublic relationsSocial scienceAnthropologyBusiness

Abstract

fetched live from OpenAlex

internationale critically examines where international students from Asia fit within broader initiatives of internationalization, Indigenization and decolonization, and equity, diversity, and inclusion in Canada’s colleges and universities. The goal is to contextualize some of the challenges they face, to understand how they fit within institutional priorities, and to examine knowledges, strategies, structures, and spaces from critical perspectives. The seven papers in this issue, as described in the next section, were selected for their examination and exposition of the connections and disconnections between actors in the domain of international education and for provoking questions about the lack of coherence among internationalization, Indigenization, and equity priorities within institutions. In this section, we offer a justification for the focus on students from Asia, namely India and China, and argue for situating their experiences of recruitment, exclusion, and marginalization within a decolonizing and equity, diversity, and inclusion framework.

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)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.434
Threshold uncertainty score0.808

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0020.001
Scholarly communication0.0080.005
Open science0.0020.003
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.4340.258

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 head0.066
GPT teacher head0.433
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 designNot applicable
Domainnot available
GenreEditorial

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

Citations1
Published2022
Admission routes3
Has abstractyes

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