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Record W200173602

The Punjabi diaspora in a time of media hybridization: The empowering of ‘counterpublics’

2012· dissertation· en· W200173602 on OpenAlexaboutno aff
Paul Edward Fontaine

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2012
Typedissertation
Languageen
FieldSocial Sciences
TopicDiaspora, migration, transnational identity
Canadian institutionsnot available
Fundersnot available
KeywordsMainstreamFraming (construction)DiasporaMulticulturalismMedia studiesNegotiationNarrativeSociologyGender studiesCommunity cohesionPolitical scienceSocial scienceGeography
DOInot available

Abstract

fetched live from OpenAlex

This thesis explores the ways in which three Punjabi-Canadian news outlets in British Columbia push back against negative representations in the mainstream press, while drawing attention to causes of concern to members of that diasporic community in Canada and around the world.
\nI argue that the three outlets reflect the formation of “public sphericules,” which both provide counter-narratives to mainstream discourse and offer coverage that attempts to integrate members of that diasporic group into mainstream Canadian society. These are important roles for a number of reasons; because of the negative representations of South Asians that have characterized the Canadian mainstream press’ coverage; and because multicultural news outlets help people to negotiate between their physical and cultural homes. Scholars in the areas of diasporic studies, South Asian studies, and counterpublic formation inform this thesis.
\nThrough qualitative interviews with the editors at each of the publications, as well as through a two-month framing analysis of the coverage at the outlets, this study explores how multiple public sphericules can be bonding agents, building a sense of cohesion within a cultural community, while at the same time bridging that cultural community with the larger communities in which they live.

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)0.004
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.403
Threshold uncertainty score0.895

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.001
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 head0.026
GPT teacher head0.311
Teacher spread0.284 · 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 designObservational
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

Citations0
Published2012
Admission routes1
Has abstractyes

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