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Record W2995078295 · doi:10.1111/nana.12574

‘It's (not) who we are’: Representing the nation in US and Canadian newspaper articles about refugees entering the country

2019· article· en· W2995078295 on OpenAlexaboutno aff
Bernadette Nadya Jaworsky

Bibliographic record

VenueNations and Nationalism · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsnot available
Fundersnot available
KeywordsNewspaperRefugeeNational identityNationalismPoliticsNarrativeSociologyIdentity (music)HarmGender studiesImmigrationPolitical scienceMedia studiesPolitical economyLawAesthetics

Abstract

fetched live from OpenAlex

Abstract Ideas about nation and national identity continue to be highly important, for both individuals and collectivities. In this article, I provide a cultural‐sociological reconstruction of the meanings of national identity conveyed in US and Canadian newspaper coverage of refugees entering, or potentially entering, the country. I engage Billig's theory of ‘banal nationalism’ and Anderson's idea of ‘imagined comEties’. In Canada, there is a single narrative that encapsulates ‘who we are’—a generous, welcoming country for people fleeing extraordinarily difficult circumstances, who will eventually integrate and succeed. National identity is processual, narrated as an ongoing ‘national project’. In the US, there are two distinct storylines about ‘who we are not’. Both begin with the country depicted as a humanitarian leader but diverge along political party lines. The Democrats quoted invoke history and ‘American values’ to say this is not a country that turns its back on those in need; the Republicans argue that this is not a country that exposes its people to harm, so refugee admissions must be halted. National identity is more solid, represented in passing; ‘who we are’ is taken for granted by spelling out ‘who we are not’.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.826
Threshold uncertainty score0.945

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.016
GPT teacher head0.285
Teacher spread0.270 · 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 designTheoretical or conceptual
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

Citations4
Published2019
Admission routes1
Has abstractyes

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