MétaCan
Menu
Back to cohort
Record W4250908267 · doi:10.22215/etd/2015-11119

Knots That Strain and Threads That Bind: NGO-Grassroots Dynamics in the Movement Web Challenging Canadian Resource Extractivism

2015· dissertation· en· W4250908267 on OpenAlexaffabout
Maximilian Chewinski

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsCarleton University
Fundersnot available
KeywordsGrassrootsAmbivalenceResource mobilizationSocial movementCollective actionSocial movement theoryFraming (construction)Political scienceSociologyPublic relationsGender studiesSocial psychologyEngineeringPoliticsPsychologyLaw

Abstract

fetched live from OpenAlex

The predominant NGOization thesis typically describes NGO grassroots relationships as disconnected, with the former engaging in hegemonic social formations, the apolitical delivery of services, and shifting accountability structures that alter agendas for social change. By utilizing a hermeneutic phenomenological approach, and in conducting nine in depth interviews with two NGOs and two grassroots groups working within this field, the objective of this thesis is to complicate theories of NGOization by magnifying the threads and knots that comprise the social movement web challenging Canadian resource extractivism. In light of five knots that create tensions and feelings of ambivalence between NGOs and grassroots groups, my findings suggest that they are bound together in this web through five main threads. This thesis asserts that studies on NGOization would benefit from the relational understanding of collective action provided by social movement studies, including the conceptual tools offered through resource mobilization and framing theories.

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)0.005
metaresearch head score (Gemma)0.009
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 score0.431
Threshold uncertainty score0.866

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0280.046
Scholarly communication0.0190.008
Open science0.0020.010
Research integrity0.0020.004
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 head0.045
GPT teacher head0.319
Teacher spread0.274 · 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
Published2015
Admission routes2
Has abstractyes

Explore more

Same topicNonprofit Sector and VolunteeringFrench-language works237,207