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Record W2327120505 · doi:10.1177/2057047315625076

Evolving repertoires: Digital media use in contentious politics

2016· article· en· W2327120505 on OpenAlexfundno aff
Emad Khazraee, James Losey

Bibliographic record

VenueCommunication and the Public · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsnot available
FundersUniversity of TorontoKent State University
KeywordsDemocratizationPoliticsThe InternetSocial mediaContentious politicsContext (archaeology)Digital mediaSocial movementMosaicSociologyPolitical scienceMedia studiesComputer scienceWorld Wide WebDemocracyLawHistory

Abstract

fetched live from OpenAlex

The spread of the Internet coupled with knowledgeable users has led to the use of digital media as a tool for advocacy and activism. Building on theoretical foundations of eventful histories and digital formations, this article investigates the interrelated nature of contentious politics and digital technologies. Our analysis documents the eventful history of changing digital repertoires of contention in the context of messaging, blogging, and social networking sites in Iran. We argue that investigating single moments of protest offers only snapshots of how digital technologies are used in contentious politics, and entails the risk of focusing on a single platform rather than the mosaic of online and offline repertoires. We demonstrate that documenting event histories challenges the assumptions of the emancipatory nature of a specific technology by revealing the changing efficacy of repertoires during different moments of contention; therefore, we should avoid assigning stable causal relations between digital technologies and the democratization processes of societies.

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)0.004
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0060.003
Science and technology studies0.0040.011
Scholarly communication0.0080.009
Open science0.0010.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.065
GPT teacher head0.302
Teacher spread0.237 · 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 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

Citations24
Published2016
Admission routes1
Has abstractyes

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