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Increasing Awareness

2020· book-chapter· en· W3085932922 on OpenAlexaffabout
Daphne Lordly, Jennifer L. Guy, Yue Li

Bibliographic record

VenueAdvances in higher education and professional development book series · 2020
Typebook-chapter
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsMount Saint Vincent University
Fundersnot available
KeywordsMulticulturalismAcculturationIdentity (music)CurriculumPedagogyFood cultureSociologyCultural identityPublic relationsPsychologyPolitical scienceSocial scienceEthnic groupAestheticsAnthropology

Abstract

fetched live from OpenAlex

The authors situate student food experience as a key source of tension for international students. Multicultural food learning activities (MFLAs) are positioned as spaces for cultural connection and knowledge exchange. Through a review of relevant literature, three themes emerge: 1) food, diet and culture, 2) acculturation and identity through social connections with food, and 3) the implications of lack of food on culture, identity, and well-being. Reflecting on the authors' personal applications of MFLAs within nutrition curricula and a student-led society supporting cultural integration, the implications of such a learning platform are illuminated. In response to emergent themes, the authors share observations and make recommendations for university-based programming and future research. The authors urge academic communities to consider the complexity and impact of student food experiences when contemplating the international student experience in Canada. Food learning and experience-based platforms are opportunities to support student culture and identity.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.083
Threshold uncertainty score0.278

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0050.006
Open science0.0010.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0830.016

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 head0.039
GPT teacher head0.362
Teacher spread0.323 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations3
Published2020
Admission routes2
Has abstractyes

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