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Record W4408654846 · doi:10.5040/9781666994827

International Students in Higher Education

2021· book· en· W4408654846 on OpenAlexaboutno aff
Vander Tavares

Bibliographic record

Venuenot available
Typebook
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationPsychologyPolitical sciencePedagogy

Abstract

fetched live from OpenAlex

<JATS1:p>Applying a critical and holistic framework, International Students in Higher Education: Language, Identity, and Experience from a Holistic Perspective explores the lived experiences of four multilingual international students at a Canadian University. Vander Tavares investigates how the students experience life in the host community, construct their multiple identities, and develop their proficiency in the English language. Additionally, Tavares examines the ways in which the institutional context impacts the students’ experiences by foregrounding the voices of domestic students, faculty and support staff, and their experiences and perspectives on international students, multiculturalism, and multilingualism. Drawing from theories of cultural and social psychology, post-structuralism, multilingualism, and a sociocognitive approach to second language acquisition, this project presents rich ethnographic portraits of the students’ experiences. This contextualised and humanised portrayal of the students’ individual journeys challenges the prevailing discourse of deficit that has traditionally been used to characterise the multilingual international student experience. Overall, this book demonstrates that an ethical understanding of multilingual international students’ experiences requires critical attention to the sociocultural and sociolinguistic contexts of their academic communities and a holistic approach that considers the multiple domains of lived experience that the students navigate.</JATS1:p>

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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.728
Threshold uncertainty score0.974

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0270.000

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 head0.060
GPT teacher head0.397
Teacher spread0.337 · 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
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

Citations7
Published2021
Admission routes1
Has abstractyes

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