MétaCan
Menu
Back to cohort
Record W4210304320 · doi:10.1007/s11159-021-09932-8

Media and government framing of asylum seekers and migrant workers in Canada during the COVID-19 pandemic

2021· article· en· W4210304320 on OpenAlexaffabout
Michelle Stack, Amea Wilbur

Bibliographic record

VenueInternational Review of Education · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and experiences of immigrants and refugees
Canadian institutionsUniversity of the Fraser ValleyUniversity of British Columbia
Fundersnot available
KeywordsRefugeeDignityFraming (construction)SociologyImmigrationGovernment (linguistics)Political scienceCoronavirus disease 2019 (COVID-19)PandemicGender studiesCriminologyLawMedicineHistory

Abstract

fetched live from OpenAlex

Abstract One understudied area of adult education and lifelong learning is the role of media as educator and policy player. This article describes how the authors used critical discourse analysis to examine how asylum seekers, migrant workers and their advocates have challenged long-standing discursive framings of them as benefactors of Canadian generosity, criminals, burdens or victims – during the first ten months of the COVID-19 pandemic. The analysis points to the difficulties of navigating media engagement to advocate for individuals facing deportation from Canada, while also attempting to challenge the dichotomy of people seen either as worthy of dignity (those who work for low pay and in dangerous conditions to care for Canadians) or as unworthy (those who work on farms or who are not able to work). However, it also reveals the potential for critical lifelong media education to inform the work of adult educators across classroom, labour and social movement contexts to disrupt exclusionary and oppressive media and government narratives.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.442
Threshold uncertainty score0.468

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0000.000

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 head0.017
GPT teacher head0.338
Teacher spread0.321 · 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.

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

Citations0
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueInternational Review of EducationSame topicEducation and experiences of immigrants and refugeesFrench-language works237,207