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Record W4372347658 · doi:10.1017/s0003055423000291

Coordinated Dis-Coordination

2023· article· en· W4372347658 on OpenAlexfundno aff
Mai Hassan

Bibliographic record

VenueAmerican Political Science Review · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Conflict and Governance
Canadian institutionsnot available
FundersCenter for Advanced Study, University of Illinois at Urbana-ChampaignUniversity of EssexUniversity of VirginiaYork UniversityVanderbilt UniversityCenter for Advanced Study in the Behavioral Sciences, Stanford UniversityUniversity of MichiganYale University
KeywordsMobilizationCollective actionSocial movementPolitical scienceSocial mobilizationField (mathematics)The InternetWork (physics)Movement (music)Action (physics)Political economyPublic relationsSociologyLawEngineeringPoliticsComputer science

Abstract

fetched live from OpenAlex

Dissidents mobilizing against a repressive regime benefit from using public information for tactical coordination since widespread knowledge about an upcoming event can increase participation. But public calls to protest make dissidents’ anticipated activities legible to the regime, allowing security forces to better stifle mobilization. I examine collective action during Sudan’s 2018–19 uprising and find that mobilization appeared to be publicly coordinated through social movement organizations and internet and communicative technology, consistent with common channels identified by existing literature. Yet embedded field research reveals that some dissidents independently used public calls to secretly organize simultaneous contentious events away from publicized protest sites, perceiving that their deviations would make the regime’s repressive response relatively less efficient than the resulting efficiency losses on the movement’s mobilization. These findings push future work to interrogate more deeply the mechanisms by which dissidents use coordination channels that are also legible to the regime they are mobilizing against.

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)0.002
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.952
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.006
Science and technology studies0.0010.006
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.034
GPT teacher head0.414
Teacher spread0.380 · 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 teacher head, not a consensus.

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

Citations10
Published2023
Admission routes1
Has abstractyes

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