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Record W2605220065 · doi:10.1007/s11266-017-9867-8

Civil Society and Social Innovation in Public Arenas in Brazil: Trajectory and Experience of the Movement Against Electoral Corruption (MCCE)

2017· article· en· W2605220065 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

VenueVOLUNTAS International Journal of Voluntary and Nonprofit Organizations · 2017
Typearticle
Languageen
FieldComputer Science
TopicOpen Source Software Innovations
Canadian institutionsConcordia University
FundersUniversidade do Estado de Santa CatarinaConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsLanguage changeMultitudeCivil societySocial movementPolitical scienceSocial mobilizationPublic sphereSocial changePolitical economyPublic administrationPublic relationsSociologyLawPolitics

Abstract

fetched live from OpenAlex

Abstract In recent decades, the Brazilian Movement Against Electoral Corruption (MCCE) has been promoting social innovation in the public sphere, which led to mobilization towards the creation of two popular initiatives in Brazil: the “Law Against Vote-Buying” (Law 9840/1999) and the “Clean Record Law” (Complementary Law 135/2010). This paper explores how the collectives of MCCE engage in social innovation in the public arena of electoral corruption in Brazil. The analysis shows social innovation as a driving force of social change promoted by the association of a multitude of actor networks both in the long term and at the interface of macro and microscales of social reality. Therefore, social innovation in the Brazilian electoral corruption arena occurs simultaneously as a process and an outcome produced by the collective actions of different public groups that can reflect, organize and reform a cause, manage trial situations and create new solutions for this public problem.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.460

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.018
GPT teacher head0.280
Teacher spread0.262 · 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