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Record W2102010703 · doi:10.1080/13621025.2015.1006581

Mechanisms for popular participation and discursive constructions of citizenship

2015· article· en· W2102010703 on OpenAlexaff
Pascal Lupien

Bibliographic record

VenueCitizenship Studies · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsCitizenshipCitizen journalismLatin AmericansSociologyState (computer science)Active citizenshipPoliticsGender studiesPerceptionInterpretation (philosophy)Discourse analysisParticipatory action researchPolitical scienceLawEpistemologyAnthropology

Abstract

fetched live from OpenAlex

In the past decade, Latin America has witnessed the emergence of a political discourse that links popular participation to citizenship accompanied by an explosion of participatory mechanisms. Yet there is little qualitative research that looks at how participatory experiences affect people's perceptions of their role as citizens or to what extent the discourse transmitted through these institutions encourages participation or compliance. This article examines conceptions of citizenship among individuals who engage in participatory mechanisms in Venezuela, Ecuador and Chile. Using discourse analysis, it finds that participants in Venezuela and Ecuador have developed a ‘radical’ conception of active citizenship that differs from the liberal interpretation in Chile. Regardless of the preferred model, however, state discourse establishes parameters around citizenship. Furthermore, the discursive repertoires of citizen participants align with those produced by state institutions, suggesting that participatory mechanisms act to socialize people into participating in ‘legitimate’ and acceptable ways.

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)0.018
metaresearch head score (Gemma)0.023
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.023
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0060.004
Science and technology studies0.0100.058
Scholarly communication0.0140.013
Open science0.0020.011
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.197
GPT teacher head0.420
Teacher spread0.223 · 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

Citations7
Published2015
Admission routes1
Has abstractyes

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