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Record W4407145326 · doi:10.13185/2799-015x.1046

Transnational Ideas and Connections: Understanding Asian Civil Society Activism

2014· article· en· W4407145326 on OpenAlexaff
Dominique Caouette, Clara Boulianne Lagacé, Denis Côté

Bibliographic record

VenueSocial Transformations Journal of the Global South · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicAsian Geopolitics and Ethnography
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsCivil societyPolitical scienceGender studiesSociologyPolitical economyPoliticsLaw

Abstract

fetched live from OpenAlex

Whether one looks back at armed insurgency movements, the Philippines’ People Power, or Jakarta’s riots against Suharto, transnational ideas, models of collective action, and activists have been keys in inspiring and fostering civil society mobilization and organizations in Southeast Asia. What are some of the common characteristics of Asian civil society activism, and what are some of the differences? Can we explain these similitudes and differences across countries, especially within Southeast Asia? Are there themes for activism that are more dominant than others? To answer these questions, we first undertook a short historical and comparative review of social activism in the region before conducting a preliminary analysis of a database on NGOs, networks, and coalitions in various Southeast Asian countries. Our results seem to show that national organizations tend to be influenced by agenda setting on the part of regional organizations, to the point where it might trump the importance of national/local issues, such as the regime type, and might homogenize the issues on which organizations work across countries. At the same time, national/local animosities also influence regional organizations, whether they want it or not. In sum, regional and national organizations shape each other, and that the influence is far from going only in one single direction.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.839
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.030
GPT teacher head0.288
Teacher spread0.258 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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