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Record W2972251789 · doi:10.1080/00221546.2019.1587977

News Media Representations of International and Refugee Postsecondary Students

2019· article· en· W2972251789 on OpenAlexaffabout
Tim Anderson

Bibliographic record

VenueThe Journal of Higher Education · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and experiences of immigrants and refugees
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsInternationalizationFraming (construction)Thematic analysisHigher educationPolitical scienceSociologyPublic relationsPedagogyQualitative researchSocial scienceBusinessGeography

Abstract

fetched live from OpenAlex

Postsecondary institutions in the global north have rapidly internationalized, driven mainly by the proliferation of international students and responses to these changing demographics. This internationalization has captured attention across various platforms, including increased media focus directed toward these students and the primary and ancillary influences of their participation in PSIs and surrounding communities. The project explored in this paper examines this phenomenon by connecting internationalization of higher education research with insights from critical media studies and framing theory to investigate news media representations of international and refugee students’ participation in Canadian universities and colleges. A critical thematic analysis was performed on 391 news media texts published between 2000 and 2017. Findings reveal the Canadian news media’s tendency to construct issues related to international students and internationalization into one or a combination of four broad macrothemes: (1) Canada as benevolent and ideal; (2) international students and internationalization as commodified assets; (3) international students and internationalization as threats; and (4) the strategic neutrality of data.

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)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.310
Threshold uncertainty score0.616

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.005
Science and technology studies0.0060.004
Scholarly communication0.0060.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.386
Teacher spread0.372 · 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 designQualitative
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

Citations18
Published2019
Admission routes2
Has abstractyes

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