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Record W4393981157 · doi:10.1075/msw.00045.sha

Metaphors for multiculturalism in the Canadian context

2024· article· en· W4393981157 on OpenAlexaffabout
Kayvan Shakoury, Frank Boers

Bibliographic record

VenueMetaphor and the Social World · 2024
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsWestern University
Fundersnot available
KeywordsMulticulturalismMetaphorContext (archaeology)SociologyEnvironmental ethicsEpistemologyGeographyLinguisticsPhilosophyPedagogyArchaeology

Abstract

fetched live from OpenAlex

Abstract Although Canada is reputed for being a multicultural society, Canadians’ opinions vary regarding the extent to which multiculturalism should be promoted. Examining metaphorical language in discourse about multiculturalism may reveal which metaphors are typically used to endorse it and which ones are typically used to express a more skeptical stance. This study analyzed 646 opinion pieces regarding multiculturalism published in Canadian newspapers. Linguistic metaphors were identified and then grouped under themes. The texts were categorized according to the authors’ stance, and instantiations of the metaphor themes were tallied to determine if some occur more frequently in discourse that promotes multiculturalism compared to discourse that expresses reservations. Some metaphor themes were instantiated more often either in texts painting a positive picture of multicultural society (e.g., a multicultural society is a varied, multi-component work of art or craft ) or in ones expressing reservations (e.g., multiculturalism is a destabilizing or divisive force ). Such contrasts were nonetheless attenuated by the way a single metaphor theme can be used to serve different rhetorical purposes. It also appears that writers are not always aware of the entailments of the metaphors they use, especially if these are conventionalized phrases.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.790
Threshold uncertainty score0.891

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.031
GPT teacher head0.324
Teacher spread0.293 · 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 designNot applicable
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

Citations2
Published2024
Admission routes2
Has abstractyes

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