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Record W2884596076 · doi:10.1007/s11266-018-0023-x

Coordinating Action: NGOs and Grassroots Groups Challenging Canadian Resource Extraction Abroad

2018· article· en· W2884596076 on OpenAlexafffundabout
Max Chewinski

Bibliographic record

VenueVOLUNTAS International Journal of Voluntary and Nonprofit Organizations · 2018
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of CanadaBarrick Gold Corporation
KeywordsGrassrootsSocial movementCollective actionPublic relationsScholarshipAccountabilityPolitical scienceAction (physics)Public administrationSociologyResource (disambiguation)LawPolitics

Abstract

fetched live from OpenAlex

Abstract Research on the role of non-governmental organizations (NGOs) in collective action predicts they will not interact with grassroots groups, citing partnerships with corporations and states, the apolitical delivery of social services and accountability towards donors as disconnecting professionalized actors from volunteer-based grassroots groups. Using interviews with core activists in the movement confronting Canadian resource extraction abroad, I depart from this approach by investigating the mechanisms, or threads, that bind organizations into coordinated action. I find that NGOs and grassroots groups coordinate as a result of: shared values and environmental justice frames; the allocation of resources; and engagement in complimentary forms of advocacy driven by a division of labour and a diversity of tactics. My research develops existing approaches to theorizing coordinated action and invites scholarship on NGOization to include the conceptual toolkit provided by social movement theories to better account for NGO–grassroots dynamics.

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)0.008
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score0.604

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0510.024
Scholarly communication0.0100.002
Open science0.0020.009
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0050.000

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 head0.009
GPT teacher head0.252
Teacher spread0.244 · 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 designNot applicable
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

Citations7
Published2018
Admission routes3
Has abstractyes

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Same venueVOLUNTAS International Journal of Voluntary and Nonprofit OrganizationsSame topicMining and Resource ManagementFrench-language works237,207