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Record W3189350692 · doi:10.1515/npf-2020-0003

Factors Shaping Public Perceptions of Market-based Activities Undertaken by Canadian Nonprofits

2021· article· en· W3189350692 on OpenAlexaffabout
Aaron Turpin, Micheal L. Shier, Femida Handy

Bibliographic record

VenueNonprofit Policy Forum · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPerceptionAccountabilityTransparency (behavior)Market orientationPublic relationsBusinessMarketingService (business)Public service motivationPublic sectorPolitical sciencePsychology

Abstract

fetched live from OpenAlex

Abstract Charitable nonprofits are engaging at increasing rates in market-based activities. This study examined Canadian public perception of nonprofits’ market-based activities. Latent variables for trust, financial accountability, transparency, direct and general familiarity, understanding of nonprofit roles in service delivery and advocacy, and orientation towards market-based activities were created using a secondary dataset of nationally representative Canadians (n = 3853). Results show that positive perceptions of market-based activities of nonprofits are influenced by familiarity of nonprofits, accepting their advocacy role, and perceiving them as being accountable. Those with stronger views of nonprofits as providers of direct service had unfavorable perceptions of the nonprofit’s market-based activities. The findings have implications for nonprofit managers who engage in market-based activities and want to promote a positive orientation to these endeavors to engage consumers and investors.

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)0.003
metaresearch head score (Gemma)0.008
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.979
Threshold uncertainty score0.149

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0040.002
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0000.001
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 head0.062
GPT teacher head0.328
Teacher spread0.266 · 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

Citations4
Published2021
Admission routes2
Has abstractyes

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