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Record W2520010989 · doi:10.1080/17448689.2016.1213508

Participatory citizenship in the making? The multiple citizenship trajectories of participatory budgeting participants in Brazil

2016· article· en· W2520010989 on OpenAlexaff
Françoise Montambeault

Bibliographic record

VenueJournal of Civil Society · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Policy and Administration Research
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsCitizenshipCitizen journalismArgument (complex analysis)ScholarshipDemocracySociologyParticipatory budgetingCivil societyPoliticsActive citizenshipPolitical sciencePublic administrationPublic relationsLaw

Abstract

fetched live from OpenAlex

Most scholarship on participatory budgeting (PB) has highlighted its impact on democratic processes and redistributive outcomes, but there is also an implicit argument associating it with citizenship learning processes at the individual and collective levels. As a mechanism for social interactions, it is often called a ‘school of citizenship’ nurturing the development of ‘better citizens’ who participate as agents and members of a political community. This relation is, however, more ambiguous in practice. The article looks at this relationship and at the rise of so-called participatory democratic citizenship. Drawing from surveys conducted among PB participants in two Brazilian cities in 2009 (Porto Alegre) and 2014 (Belo Horizonte), the article shows that, at the individual level, multiple trajectories of citizenship can emerge among participants and can coexist in participatory processes. Contrary to the common wisdom, the article brings to light the complexity of differentiated citizenship learning processes among individuals active in participatory mechanisms. These cases thus show that PB does not necessarily contribute to the creation of a civic community, that is, a durable and active form of social organization that fosters the rise of a participatory and democratic citizenship.

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)0.014
metaresearch head score (Gemma)0.007
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 score0.068
Threshold uncertainty score0.785

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.182
GPT teacher head0.427
Teacher spread0.245 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations45
Published2016
Admission routes1
Has abstractyes

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