MétaCan
Menu
Back to cohort
Record W2773170277 · doi:10.1177/0002764217744839

Foundation Funding of the Environmental Movement

2017· article· en· W2773170277 on OpenAlexaff
J. Craig Jenkins, Jason T. Carmichael, Robert J. Brulle, Heather R. Boughton

Bibliographic record

VenueAmerican Behavioral Scientist · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental Justice and Health Disparities
Canadian institutionsMcGill University
Fundersnot available
KeywordsFoundation (evidence)MainstreamEliteLiberian dollarPolitical scienceSocial movementSocial justiceEnvironmental movementCritical appraisalPublic administrationSociologyLawSocial sciencePoliticsMedicineEconomicsFinanceAlternative medicine

Abstract

fetched live from OpenAlex

We address the long-standing debate between elite theorists and pluralists about the priorities and scale of foundation funding for social movements by examining systematic data on foundation grants to environmental movement organizations (EMOs) between 1961 and 2000. By combining these data with a comprehensive inventory of EMOs that operated in this period, we show that foundation giving favored conservative mainstream environmental discourses, EMOs that avoided protest, older EMOs, and those located in the northeastern seaboard. Despite major growth in the constant dollar value of foundation giving to EMOs, this remains a highly concentrated system of philanthropy with over half of all foundation grants going to the top 20 grant recipients, a third of which have been leading recipients for over five decades. Nonetheless, there is evidence of change in that alternative discourses, especially environmental justice, received over 5% of these grants in 2000.

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)0.006
metaresearch head score (Gemma)0.026
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 score0.014
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.046
GPT teacher head0.376
Teacher spread0.329 · 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

Citations54
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Behavioral ScientistSame topicEnvironmental Justice and Health DisparitiesFrench-language works237,207