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Record W4308624687 · doi:10.3167/cont.2022.100203

Drive-By Solidarity

2022· article· en· W4308624687 on OpenAlexaff
Jared M. Wright, Kaitlin Kelly-Thompson, S. Laurel Weldon, Dan Goldwasser, Rachel L. Einwohner, Valeria Sinclair‐Chapman, Fernando Tormos‐Aponte

Bibliographic record

VenueContention · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsSolidarityIdentity (music)Social solidaritySociologyCollective identityTerm (time)Social psychologyPolitical sciencePsychologyLawAestheticsPhilosophySocial science

Abstract

fetched live from OpenAlex

This article offers a theoretical and empirical exploration of a form of solidarity in which one group spontaneously mobilizes in support of another, unrelated group. It is a fleeting solidarity based not on shared identity but on temporarily aligned goals, one aimed less at persistence and more at short-term impact. We call this drive-by solidarity because of its spontaneous, unilateral, and unsolicited nature. We argue that it is a “thinner” form of solidarity in comparison to “thicker” forms usually conceptualized in the social movement literature. We examine the case of Anonymous’s “Operation KKK” (#OpKKK), an online hacktivist campaign to expose Ku Klux Klan members carried out in support of #BlackLivesMatter protesters in Ferguson, Missouri, in November 2014, and we use social media data to show that, while BLM and Anonymous networks temporarily coordinated during the protests, there is no subsequent evidence of long-term coordination.

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)0.005
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0070.014
Scholarly communication0.0070.007
Open science0.0010.011
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0090.001

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 head0.042
GPT teacher head0.326
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

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 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

Citations7
Published2022
Admission routes1
Has abstractyes

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