MétaCan
Menu
Back to cohort
Record W4406871514 · doi:10.3390/admsci15020037

The Radicalness of Innovation in Nonprofit Community Sport Organizations

2025· article· en· W4406871514 on OpenAlexafffund
Alison Doherty, Larena Hoeber, Orland Hoeber, Kristen A. Morrison, Richard Wolfe

Bibliographic record

VenueAdministrative Sciences · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsUniversity of VictoriaUniversity of WindsorUniversity of ReginaWestern University
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Regina
KeywordsBusinessPublic relationsNonprofit organizationMarketingPolitical science

Abstract

fetched live from OpenAlex

Our study examined and compared the type, process, conditions, and consequences of radical and incremental innovations in community sport organizations (CSOs), which are a type of nonprofit membership association. Interviews were conducted with the president (or representative) of 14 CSOs engaged with both radical and incremental innovations. Radical innovations were reported to be mostly technical (but also administrative), undertaken with the goal of club growth and enhancing club management, adopted and further adapted from outside the organization, influenced by the culture and expertise of the board and the culture and capacity of the CSO at large, and informed by market opportunity and best practices. The radical innovations were reported to be successful in reaching their intended goals, and a wide variety of unanticipated (positive) consequences was also realized. The findings have implications for the management of radical (and incremental) innovation in the focal nonprofit context and contribute to theorizing about the radicalness of organizational innovation.

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)0.008
metaresearch head score (Gemma)0.032
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 score0.008
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.032
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0030.007
Scholarly communication0.0040.003
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.061
GPT teacher head0.409
Teacher spread0.348 · 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
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueAdministrative SciencesSame topicNonprofit Sector and VolunteeringFrench-language works237,207