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Record W4399036139 · doi:10.29173/cjnser674

Social Innovation and Nonprofit Resource Provision: A Discourse Analysis

2024· article· en· W4399036139 on OpenAlexaffvenue
Aaron Turpin, Micheal L. Shier

Bibliographic record

VenueCanadian journal of nonprofit and social economy research · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsResource (disambiguation)Social innovationContext (archaeology)SociologyLegitimacyResource dependence theoryStakeholderKnowledge managementTerminologyNarrativeBusinessPolitical scienceManagementPublic relationsComputer scienceEconomicsGeography

Abstract

fetched live from OpenAlex

This research adopts a resource dependency approach to support the process of social innovation application within the context of nonprofit resource procurement using a comparative sample of re- source-providing organizations (n = 8) and nonprofit resource recipients (n = 10). An organizational discourse analysis was adopted to explore concepts of power and legitimacy across groups revealing several ways that social innovation is employed and challenged by both resource recipients and providers. Further, a text coverage analysis revealed several discrepancies with the use of terminology between sub-samples. Together, these novel analytical approaches provide a narrative regarding the ways in which social innovation is co-conceptualized within nonprofit resource provision, including examining the role of language and power between stakeholder groups.

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)0.020
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.107

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.026
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0080.008
Science and technology studies0.0110.015
Scholarly communication0.0080.010
Open science0.0020.009
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.096
GPT teacher head0.426
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 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

Citations3
Published2024
Admission routes2
Has abstractyes

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Same venueCanadian journal of nonprofit and social economy researchSame topicNonprofit Sector and VolunteeringFrench-language works237,207