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Record W2912930965 · doi:10.1177/1748048519828594

Creating in-between spaces through diasporic and mainstream media consumption: A comparison of four ethnocultural and immigrant communities in Ottawa, Canada

2019· article· en· W2912930965 on OpenAlexafffundabout
Rukhsana Ahmed, Luisa Veronis

Bibliographic record

VenueInternational Communication Gazette · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicDiaspora, migration, transnational identity
Canadian institutionsUniversity of Ottawa
FundersUniversity of Ottawa
KeywordsMainstreamNewspaperImmigrationMedia consumptionConsumption (sociology)Media studiesSociologyNew mediaPolitical sciencePublic relationsSocial scienceLaw

Abstract

fetched live from OpenAlex

Media provide essential information that can help migrants settle, build local community, and maintain transnational linkages. In this study, we extend the existing literature by undertaking a unique comparative project examining the role of both diasporic and mainstream media – including print (newspapers) and broadcast (TV and radio) – in meeting the information needs of four ethnocultural and immigrant communities in Ottawa, Canada. Our analysis of survey findings shows significant variations across the four communities in their consumption of print and broadcast diasporic and mainstream media based on immigration category, time spent in Canada, and level of official language (English and French) proficiency. Adopting a uses and gratifications theoretical lens, we argue that participants embrace a more holistic approach to media use, which affords them benefits from both kinds of media resources by creating in-between spaces for participation in host societies and transnational communities.

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)0.003
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 score0.043
Threshold uncertainty score0.316

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0300.009
Scholarly communication0.0060.002
Open science0.0020.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.056
GPT teacher head0.337
Teacher spread0.281 · 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

Citations10
Published2019
Admission routes3
Has abstractyes

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