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Record W3096045284 · doi:10.1177/0163443720968173

Media scarcity in an age of information abundance: the case of MK veterans

2020· article· en· W3096045284 on OpenAlexafffund
Greg Elmer

Bibliographic record

VenueMedia Culture & Society · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicMedia Studies and Communication
Canadian institutionsToronto Metropolitan University
FundersSocial Sciences and Humanities Research Council of CanadaNederlandse Organisatie voor Wetenschappelijk OnderzoekRyerson University
KeywordsScarcityDocumentationPoliticsMedia coveragePolitical scienceState (computer science)Public relationsAbundance (ecology)SociologyMedia studiesLawEcologyEconomicsComputer science

Abstract

fetched live from OpenAlex

This paper questions the dominant attention economy and media abundance framework in media studies by questioning the mediated politics of ‘media scarce’ communities and individuals. Focusing on the armed wing of the African National Congress (MK), the paper questions how media scarcity affects a group’s ability to engage in public campaigns, given their lack of historical media documentation. The paper analyzes how the MK developed various media tactics to overcome their lack of media documentation, both during the fight against the Apartheid state and in present day campaigns for military veterans’ rights.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score0.719

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.040
GPT teacher head0.308
Teacher spread0.267 · 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 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

Citations1
Published2020
Admission routes2
Has abstractyes

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