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Record W2476148226 · doi:10.1057/9780230240865_6

Why Are Social Movement Organizations Deliberative? Structural and Cultural Determinants of Internal Decision Making in the Global Justice Movement

2009· book-chapter· en· W2476148226 on OpenAlexaboutno aff
Marco Giugni, Alessandro Nai

Bibliographic record

VenuePalgrave Macmillan UK eBooks · 2009
Typebook-chapter
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsnot available
Fundersnot available
KeywordsDeliberationDeliberative democracySocial movementDemocracyMovement (music)Political sciencePublic relationsDecision-making modelsQuarter (Canadian coin)SociologySocial psychologyPsychologyGeographyPoliticsLaw

Abstract

fetched live from OpenAlex

Decision making in social movements, and democratic visions and practices more generally, vary strongly from one movement organization to another. This chapter looks at possible explanations of such differences in internal decision making observed among organizations of the Global Justice Movement. Indeed, the adoption of a given democratic model varies a great deal across the organizations included in the study (Table 5.1). Based on information derived from the organizations’ online and offline documents, as well as a structured questionnaire submitted to them, the last column of this table shows that the associational model is the most common, followed by the two deliberative models and, lagging far behind, the assembleary model. Thus, half of the organizations put forward deliberation as their decision-making mode; about one-quarter of them follow the deliberative participative model. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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)0.011
metaresearch head score (Gemma)0.030
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 score0.011
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.030
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0040.016
Scholarly communication0.0090.004
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.025
GPT teacher head0.326
Teacher spread0.301 · 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

Citations6
Published2009
Admission routes1
Has abstractyes

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Same venuePalgrave Macmillan UK eBooksSame topicSocial Media and PoliticsFrench-language works237,207