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Record W4394575594 · doi:10.1177/13505084241238278

Trolling the Leviathan: How the use of social media by democratic organizations engenders monsters

2024· article· en· W4394575594 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueOrganization · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsLEVIATHAN (cipher)DemocracySociologySocial mediaPolitical scienceEpistemologyLawComputer securityPoliticsComputer sciencePhilosophy

Abstract

fetched live from OpenAlex

The proliferation of trolls may be one of the main reasons why democratic organizations fail to use social media to renew. The literature predominantly assimilates these trolls to psychologically deviant individuals. This article questions this individual-centric approach by suggesting that trolls may well be socially constructed organizational monsters. To investigate this phenomenon, for 2 years, we studied the interactions on a Facebook group between the leaders and members of a trade union. We identified three bi-directional effects at the heart of what we call the monstrification process: discording, disordering, and disgusting effects. The paper contributes to the troll and organizational monster literature by evidencing the four-stage process through which trolls are organizationally constructed as deviant online participants. Our work also adds to the democratic organization literature by metaphorically underlining actors’ emotional and moral distress caused by the dysfunctional encounter of offline and online democracy.

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.

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)0.000
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.362
Threshold uncertainty score0.717

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.048
GPT teacher head0.269
Teacher spread0.221 · 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