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Record W1570757902 · doi:10.22230/cjc.2008v33n4a2030

Racializing the Audience: Immigrant Perceptions of Mainstream Canadian English-Language TV News

2008· article· en· W1570757902 on OpenAlexaffvenueabout
Minelle Mahtani

Bibliographic record

VenueCanadian Journal of Communication · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicMedia Studies and Communication
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMainstreamEthnic groupImmigrationMulticulturalismPerceptionMedia studiesSociologyGender studiesSpace (punctuation)AdvertisingPolitical scienceLinguisticsPsychologyAnthropology

Abstract

fetched live from OpenAlex

This paper offers an analysis of a pilot project that examines the perceptions of English-language TV news among two racialized groups: self-identified Iranian-Canadians and Chinese-Canadians. This research indicates that, according to participants, mainstream Canadian English-language TV news does not necessarily offer racialized immigrant audiences a space through which to see themselves reflected accurately as part of Canada’s rich social life beyond the celebration of ethnic events and festivals. Participants explained that they appreciated Canadian English-language television news, with important caveats. They would like to see the Canadian English-language television news media create spaces in which they could see their own ethnic, racial, cultural, and immigrant identities reflected within the backdrop of the Canadian multicultural state.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.184
Threshold uncertainty score0.370

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0110.003
Scholarly communication0.0040.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.025
GPT teacher head0.286
Teacher spread0.261 · 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 source (direct Gemma or distilled Codex), 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

Citations19
Published2008
Admission routes3
Has abstractyes

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