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Record W2917590359 · doi:10.3138/chr.2017-0133

Welcoming International and Foreign Students in Canada: Friendly Relations with Overseas Students (<scp>fros</scp>) at the University of Toronto, 1951–68

2019· article· en· W2917590359 on OpenAlexaffvenueabout
Daniel Poitras

Bibliographic record

VenueCanadian Historical Review · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNarrativeGlobalismAgency (philosophy)SociologyOpenness to experienceTransformative learningMeaning (existential)PedagogyMedia studiesGender studiesPolitical sciencePsychologyGlobalizationSocial scienceSocial psychologyLaw

Abstract

fetched live from OpenAlex

One organization particularly dedicated to the understanding and well-being of foreign students in Canada was Friendly Relations with Overseas Students (fros). Active during the 1950s and 1960s, fros became a symbol for Canada to assert its openness and distinctiveness in North America. As an organization focused on counselling, accommodating, and, to some extent, integrating foreign students, fros also built bridges between Canada and the rest of the world. The experience of fros members thus allows us to both deepen our understanding of foreign students’ agency and of the meaning and importance of their transnational experience for both themselves and Canada. In this article, I concentrate on the narrative about cultural exchange forged at fros by both its personnel and students. I propose that what I call the “fros narrative” was a discourse about the integration of foreign students on the University of Toronto’s campus and in the larger Canadian society. It promoted intercultural connections and a form of globalism. It also had a transformative and even redemptive dimension, as it aimed to change people’s worldviews and self-conceptions by abolishing ethnic prejudices.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: none
Teacher disagreement score0.582
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.221
Teacher spread0.212 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations5
Published2019
Admission routes3
Has abstractyes

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