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Record W2773889623 · doi:10.1177/0002764217744133

State Funding for Human Rights Activism: Channeling Protest?

2017· article· en· W2773889623 on OpenAlexaffabout
Dominique Clement

Bibliographic record

VenueAmerican Behavioral Scientist · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Society, and Development
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSocial movementPublic administrationState (computer science)Political scienceGovernment (linguistics)Altruism (biology)Human rightsPublic fundingSurvey data collectionPublic relationsPolitical economySociologyLawPoliticsSocial psychology

Abstract

fetched live from OpenAlex

Channeling theory posits that external funding for social movements, rather than coopting activism, channels activism into more structured and less militants forms. Studies on channeling, however, focus on private funding. The following article examines whether public funding has a comparable effect on social movements. Using the human rights movement in Canada as a case study, it examines several issues relating to channeling: why funders support activism; funding as social control or altruism; how funding is related to consolidating movement gains; and the impact of funding on mobilization, activism, and internal movement dynamics. To address these questions, this article draws on an innovative new data set that includes lists of grants extracted from more than 30 years of government budgets in Canada. It also draws on several years of archival research on a network of 19 organizations in every region of Canada, as well as interviews with former members of these organizations. In addition to demonstrating that public funding has a comparable channeling effect as private funding, this article provides the first comprehensive survey of the extent of state funding for the human rights movement in Canada.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.492
Threshold uncertainty score0.991

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 studies0.0100.002
Scholarly communication0.0010.000
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 head0.080
GPT teacher head0.416
Teacher spread0.336 · 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 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

Citations23
Published2017
Admission routes2
Has abstractyes

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