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Record W1994956212 · doi:10.2202/2154-3348.1000

Voice-In, Voice-Out: Constituent Participation and Nonprofit Advocacy

2010· article· en· W1994956212 on OpenAlex
Chao Guo, Gregory D. Saxton

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNonprofit Policy Forum · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsYork University
Fundersnot available
KeywordsScope (computer science)Affect (linguistics)Public relationsGovernment (linguistics)Citizen journalismBusinessExploratory researchPublic policyNonprofit sectorSample (material)Survey data collectionPolitical sciencePsychologySociology

Abstract

fetched live from OpenAlex

How do participatory constituent practices affect the scope and intensity of nonprofit advocacy? In this study, we examine this question through survey data from a random sample of charitable nonprofit organizations in Arizona in 2007. Our findings show that the scope and intensity of nonprofit advocacy tend to increase with constituent board membership, communication with constituents, and level of constituent involvement in strategic decision making. However, the scope and intensity of nonprofit advocacy tends to decrease with increased government funding and private contributions. These findings suggest important implications for organizations wishing to be more effective in influencing public policy.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.357
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.024
GPT teacher head0.359
Teacher spread0.335 · 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