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Record W2587246208 · doi:10.1177/2056305117691998

Leveraging Visibility, Gaining Capital? Social Media Use in the Fight Against Child Abusers: The Case of The Judge Beauce

2017· article· en· W2587246208 on OpenAlexaffabout
David Myles, Daniel Trottier

Bibliographic record

VenueSocial Media + Society · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsUniversité de Montréal
FundersNederlandse Organisatie voor Wetenschappelijk Onderzoek
KeywordsVisibilityScrutinyPoliticsSocial capitalSociologyPublic relationsPower (physics)LeverSocial mediaCapital (architecture)Political sciencePolitical economyLawHistoryEngineering

Abstract

fetched live from OpenAlex

This article examines the constitutive role of mediated visibility in the emergence of contemporary vigilante initiatives. Here, visibility is conceptualized as a “heuristic device” to understand social phenomena, as well as a lever for organizations to acquire various forms of capital. The article uses the case of The Judge Beauce—a Canadian organization created in 2015 to fight against child abusers—to understand how vigilante collectives can lever mediated visibility, and online visibility in particular, to acquire specific forms of policing capital (economic, social, political, and cultural). Results show that mediated visibility was indeed crucial for raising funds, constituting vigilant/e publics, and defining vigilante identities, relations, and practices. Yet, as a “double-edged sword,” mediated visibility brought on public scrutiny that simultaneously resulted in a series of liabilities. Finally, this article contends that vigilantism in the digital age should be defined as the enactment of power im(balances) through the instrumentalization of mediated visibility rather than considering force or the threat of its use as its main feature.

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 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)0.005
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.098
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0090.003
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
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 head0.085
GPT teacher head0.323
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; both teacher heads agree on what is shown here.

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

Citations8
Published2017
Admission routes2
Has abstractyes

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