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Record W3117993156

Examining the Effects of Violence and Nonviolence in Indigenous Direct Action

2020· article· en· W3117993156 on OpenAlexaboutno aff
Dorothy Hodgins

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousSocial movementSovereigntyState (computer science)Context (archaeology)Action (physics)Political scienceColonialismPower (physics)Movement (music)CriminologyPolitical economySociologyDirect actionResistance (ecology)PoliticsLawGeographyEcology
DOInot available

Abstract

fetched live from OpenAlex

A focalizing debate within social movement theory considers the efficacy of violence versus nonviolence in direct action. This is especially important to consider in a settlercolonial context where the state is systematically designed to repress mobilizations of Indigenous sovereignty. It often attempts to frame such movements as either vaguely-defined discontent or national security threats - both of which attempt to invalidate the movements’ demands. I use the case studies of the Red Power movement of the 1960s/1970s in the U.S. and the Idle No More movement of 2012/2013 in Canada to explore the ways the settler state responds to violent and non-violent forms of Indigenous resistance. It is my hope that these observed patterns of response are critically read not only by Indigenous Peoples, but also by non-Indigenous folx who wish to actively support these movements rather than passively consume and re-produce the colonial re-narrativization of movement events and intentions.

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)0.009
metaresearch head score (Gemma)0.032
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 score0.020
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.032
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0040.023
Scholarly communication0.0060.005
Open science0.0010.007
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0080.000

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 head0.022
GPT teacher head0.295
Teacher spread0.273 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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