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Record W3210533735 · doi:10.1177/15274764211052997

Political Posters Reveal a Tension in WhatsApp Platform Design: An Analysis of Digital Images From India’s 2019 Elections

2021· article· en· W3210533735 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTelevision & New Media · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsWestern University
Fundersnot available
KeywordsPoliticsAffordanceSocial mediaPolitical communicationAppealPolitical scienceContext (archaeology)SociologyPublic relationsNew mediaGlobeMedia studiesPolitical economyLawPsychology

Abstract

fetched live from OpenAlex

This article examines the effects of WhatsApp as a mode of dissemination of political posters. It found that platform affordances that control the crafting and dissemination of political messages open up the possibility of vague political messaging by conforming to the social media’s visual culture and limit the spread of these messages, restricting the ability to organically gather support for a political cause. Despite the growing appeal of social media in political campaigns, social media messages when used by individuals and small, independent social media groups, who are not a part of a larger, organized political party or movement, have little influence on electoral decisions of voters about a political cause that faces weak public support. This was discussed in the context of electoral results of the Leftist political party in India in 2019 national elections. The paper then contributes to our understanding of the extent of the influence of social media platforms on political media messages.

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.

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)0.000
metaresearch head score (Gemma)0.003
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 score0.137
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.049
GPT teacher head0.333
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