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Record W2225169648 · doi:10.4324/9781315764832-7

Theorising social media, politics and the state: an introduction

2014· book-chapter· en· W2225169648 on OpenAlexaboutno aff
Daniel Trottier, Christian Fuchs

Bibliographic record

VenueWestminsterResearch (University of Westminster) · 2014
Typebook-chapter
Languageen
FieldComputer Science
TopicCultural Insights and Digital Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsFeudalismAssemblage (archaeology)KnightDeleuze and GuattariPoliticsMedia studiesState (computer science)InstitutionSocial mediaSociologyArtPolitical scienceAestheticsHistoryLawSocial scienceArchaeologyComputer science

Abstract

fetched live from OpenAlex

This chapter explains the Deleuzo-Guattarian concepts of becoming-minoritarian, weapons and tools, and puts them together with their closely aligned concept of the assemblage. The possibility exists, then, of connecting online social media users with protest participants for the purposes of squelching dissent. Demonstrating an inherent tetravalence, Deleuze and Guattari describe the feudal assemblage, considering all the interminglings of bodies that define the institution of feudalism: The body of the earth and the social body; the body of the overlord, vassal, and serf; the body of the knight and the horse and their new relation to the stirrup; the weapons and tools assuring a symbiosis of bodies-a whole machinic assemblage. Using the latest in live streaming technology, teams of media activists from Montreal's Concordia University Television (CUTV) took to the streets, marching alongside protesters, filming the demonstrations and instantly live streaming those images to the Internet, beaming them to computers across the province, country and in fact the world.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0030.012
Scholarly communication0.0060.010
Open science0.0010.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0130.001

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 head0.103
GPT teacher head0.265
Teacher spread0.162 · 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 designTheoretical or conceptual
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
Published2014
Admission routes1
Has abstractyes

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