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Record W4307172313 · doi:10.1002/nml.21537

An application of the nonprofit virtual accountability index: Accountability in sport for development and peace

2022· article· en· W4307172313 on OpenAlex
Per G. Svensson, Michael L. Naraine

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 Management and Leadership · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsBrock University
Fundersnot available
KeywordsAccountabilityInteractivityPublic relationsThematic analysisIndex (typography)Variance (accounting)BusinessPolitical scienceMarketingSociologyQualitative researchAccountingComputer scienceWorld Wide WebSocial science

Abstract

fetched live from OpenAlex

Abstract In this research note, we examine web‐based accountability practices of human service nonprofits. Data were collected directly from the organizational websites of an international sample of 532 organizations involved in operating sport for social change programs, more commonly known as the field of sport for development and peace. Websites were coded using the nonprofit virtual accountability index—a theoretically grounded and robust tool—to measure information and interactivity available for stakeholders across five dimensions of accountability. Analyses of variance and independent t ‐tests were used to test potential group differences based on geographical region, the thematic types of social change efforts, and the type of sport used to deliver programming. The results of this analysis highlight the critical importance of geographical location and other organizational variables for web‐based accountability practices. Furthermore, the results allow nonprofit leaders to identify common areas in need of improvement for smaller and emerging nonprofits.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.098
Threshold uncertainty score0.578

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.079
GPT teacher head0.320
Teacher spread0.241 · 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