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Record W4387889484 · doi:10.3167/hrrh.2023.490304

The Yellow Vests’ Relationship to Revolution and Violence

2023· article· en· W4387889484 on OpenAlexvenueno aff
Alix Choinet

Bibliographic record

VenueHistorical Reflections/Réflexions Historiques · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicMulticulturalism, Politics, Migration, Gender
Canadian institutionsnot available
Fundersnot available
KeywordsAppropriationRevolutionary movementPoliticsSocial unrestPolitical scienceMovement (music)UnrestGovernment (linguistics)Social movementMedia studiesPolitical economySociologyLawCriminologyAesthetics

Abstract

fetched live from OpenAlex

Abstract The Yellow Vests movement, which started in France in late 2018, was unprecedented in many ways. Its use of social media to bring together individuals from all across the country, its lack of clear leadership, its refusal to work alongside political parties or unions, and its ability to bring together opinions from across the political spectrum set it apart from other periods of political and social unrest in France. Yet commentators and demonstrators alike have drawn comparisons with France's revolutionary past. Could the movement be described as revolutionary? Are the violent acts of the protestors and the violent acts of the police sufficient criteria to categorize the movement as revolutionary? Drawing from government data, reports of the demonstrations, and publications on the Yellow Vests, this article argues that their appropriation of revolutionary imagery and methods suffice to qualify some of their efforts as revolutionary, especially when considering the movement's continued impact on political and social commentary in France.

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)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0080.028
Scholarly communication0.0080.002
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.202
GPT teacher head0.440
Teacher spread0.238 · 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 designNot applicable
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
Published2023
Admission routes1
Has abstractyes

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