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Record W4323544814 · doi:10.1177/17504813231156748

A corpus-assisted discourse analysis of the representation of Syrian refugees in Canadian newspapers

2023· article· en· W4323544814 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueDiscourse & Communication · 2023
Typearticle
Languageen
FieldComputer Science
TopicHate Speech and Cyberbullying Detection
Canadian institutionsCarleton University
Fundersnot available
KeywordsRefugeeNewspaperCritical discourse analysisIdeologyRepresentation (politics)ImmigrationDiscourse analysisPoliticsSyrian refugeesSociologyCorpus linguisticsMedia studiesGender studiesPolitical scienceLinguisticsLaw

Abstract

fetched live from OpenAlex

This paper examines the representation of Syrian refugees in the Canadian press, from December 2015 to December 2017, in four English-language major newspapers. Using methods of Corpus Linguistics (CL) and Critical Discourse Analysis (CDA), this study found three prominent themes, namely intake, integration, and concern, through which Syrian refugees are depicted across the political spectrum. The results indicate that adopting a more inclusive immigration policy did not totally negate the biased and discriminatory representations entrenched in the media coverage of refugees, but it can set the stage for more empowering and sympathetic treatment of refugees in the media. This analysis speaks to the importance of media discourse in producing and maintaining particular depictions of refugees among the Canadian public, highlighting the role of ideological and political stances in the portrayals of refugees across news outlets.

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.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.380
Threshold uncertainty score0.805

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.006
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.022
GPT teacher head0.321
Teacher spread0.299 · 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