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First Nation Politics: Deprivation, Resources, and Participation in Collective Action

2004· article· en· W2135923694 on OpenAlexaffabout
Rima Wilkes

Bibliographic record

VenueSociological Inquiry · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCollective actionSocioeconomic statusMobilizationNewspaperPoliticsAction (physics)Political mobilizationUnemploymentCensusSociologyPopulationPolitical actionPolitical scienceDevelopment economicsPolitical economyEconomic growthEconomicsMedia studiesLawDemography

Abstract

fetched live from OpenAlex

How are levels of deprivation and resources associated with participation by First Nations in collective action? Although previous studies have focused on the relationship between deprivation, resources and the timing of protest, surprisingly few have used these concepts to address the issue of participation in protest. This paper presents the results of a study that compares the characteristics of First Nations with varying levels of mobilization (from participation in none to participation in several protests). Data on First Nation protest were obtained from newspapers and data on First Nation characteristics were obtained from several waves of the Canadian Census of Population. Multivariate analyses reveal that some forms of deprivation (unemployment) and resources (socioeconomic status) were related to First Nation mobilization. Explanations which synthesize theoretical concepts may, in future, provide a greater understanding of collective action than those explanations which are based on a single theory.

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.980
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.005
Scholarly communication0.0020.001
Open science0.0000.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.136
GPT teacher head0.391
Teacher spread0.255 · 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 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

Citations50
Published2004
Admission routes2
Has abstractyes

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