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Record W2147456875 · doi:10.1017/s0008423907070588

From Movements to Parties in Latin America: The Evolution of Ethnic Politics

2007· article· en· W2147456875 on OpenAlexaff
Marco Fonseca

Bibliographic record

VenueCanadian Journal of Political Science · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicPolitics and Society in Latin America
Canadian institutionsYork University
Fundersnot available
KeywordsDemocracyLatin AmericansPoliticsConsolidation (business)Ethnic groupPolitical scienceDemocratic consolidationIndigenousPolitical economySociologyGender studiesLawDemocratizationEconomics

Abstract

fetched live from OpenAlex

From Movements to Parties in Latin America: The Evolution of Ethnic Politics , Donna Lee Van Cott, Cambridge: Cambridge University Press, 2004,pp. 300. During the 1980s and 90s the English literature on Latin American politics in the Anglo North American and Anglo European academic worlds roughly evolved from works centrally concerned—and discursively interconnected—with various models of transitions to democracy to the necessary processes that the new electoral democracies had to undergo and the policies they needed to implement to advance in the process of consolidation of democracy. For scholars who essentially viewed these processes either as largely completed in institutional terms or on their way to institutional maturity and stability, the focus of scholarly attention then shifted to more subtle questions of democratic quality. Donna Lee Van Cott's From Movements to Parties in Latin America: The Evolution of Ethnic Politics is a work that not only fits into the category of works fundamentally concerned with the issues and challenges associated with either the consolidation of democracy literature or the quality of democracy literature, but it is also a work that helps to develop the literature by highlighting a central variable of Latin American culture and politics, namely, indigenous ethnic movements and politics.

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)0.002
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.889
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.003
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 head0.032
GPT teacher head0.346
Teacher spread0.314 · 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

Citations29
Published2007
Admission routes1
Has abstractyes

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