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Record W4220975745 · doi:10.1111/cag.12757

Multiple food vulnerabilities of international students from India in Greater Toronto Area colleges: A pilot study

2022· article· en· W4220975745 on OpenAlexaffvenueabout
Sutama Ghosh, Pruneah M. Kim, Raymond M. Garrison, Sohail Shahidnia

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2022
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsFood insecurityContext (archaeology)Government (linguistics)ImmigrationEconomic growthPolitical scienceInequalityFood securityPerspective (graphical)SociologyGeographyAgricultureEconomics

Abstract

fetched live from OpenAlex

Research on the food insecurity of international students, who are uniquely positioned as both newly‐arrived immigrants and post‐secondary students, is extremely limited in the Canadian context. In this paper, we attempt to create awareness about this specific form of inequality by qualitatively analyzing the experiences of 30 international students from India, in community and private colleges located in the Greater Toronto Area. Our study demonstrates that most participants faced multiple food vulnerabilities. In this regard, in addition to their internal characteristics, external circumstances have had profound impacts on their food insecurity, particularly the geographical context of the city in which they live. In turn, their food insecurity has influenced various aspects of their everyday lives, including housing, employment, and overall health and well‐being. From a policy perspective, various levels of government, and especially the Canadian post‐secondary educational institutions, must take a greater responsibility for assessing the specific needs of this marginalized group and providing necessary services.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.444
Threshold uncertainty score0.893

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0090.003
Scholarly communication0.0030.001
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.064
GPT teacher head0.326
Teacher spread0.262 · 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 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

Citations6
Published2022
Admission routes3
Has abstractyes

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