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(De)Constructing Multiculturalism: A Discourse Analysis of Immigration and Refugee System in Canadian Media

2016· article· en· W6910305408 on OpenAlexaffabout

Bibliographic record

VenueLanguage arts journal of Michigan · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsImmigrationRefugeeFraming (construction)Immigration policyMainstreamPoliticsPublic policyDiscourse analysis

Abstract

fetched live from OpenAlex

A succession of policy changes to the immigration and refugee system has been made in Canada in recent years by the Conservative federal government. Since most people’s understandings about immigration issues come from exposure to the news, the media have an important role in producing and reproducing prevalent public opinions to support and legitimize, or criticize, social and political actions. The present study examines how the immigration and refugee policy changes have been represented in mainstream print media and provides an important interface between recent political decision-making and society with regard to immigration issues. In our analysis, we demonstrate that there is a construction of the existing system as facing crisis due to rampant frauds to legitimize the implementation of more restrictive “get-tough” policies as pragmatic and commonsensical interventions. On the other hand, there is a privileging of framing immigration as being necessary for society, albeit in economic rather than sociocultural terms. In the media, social categorizations of immigrants into “good” and “bad,” and refugee claimants into “genuine” and “bogus,” are deployed to support the policy changes for a market-driven immigration system while restricting the admission of refugees and family-class immigrants, who are often portrayed as a burden on public resources.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.836
Threshold uncertainty score0.865

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.008
GPT teacher head0.288
Teacher spread0.280 · 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 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

Citations0
Published2016
Admission routes2
Has abstractyes

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