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Record W2116247489 · doi:10.1177/0899764007305052

Nonprofit and Philanthropic Studies: International Overview of the Field in Africa, Canada, Latin America, Asia, the Pacific, and Europe

2007· article· en· W2116247489 on OpenAlexaboutno aff
Roseanne M. Mirabella, Giuliana Gemelli, M.L.S. Jane Malcolm, Gabriel Berger

Bibliographic record

VenueNonprofit and Voluntary Sector Quarterly · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsnot available
Fundersnot available
KeywordsLatin AmericansEconomic growthPolitical scienceAsia pacificTraining (meteorology)Work (physics)Nonprofit organizationInternational educationHigher educationPublic administrationSociologyEconomicsGeographyEthnology

Abstract

fetched live from OpenAlex

The growth of nonprofit organizations (NPOs) and nongovernmental organizations (NGOs) around the world has been accompanied by a concomitant growth in the number of education and training programs developed to provide management training to the leaders of these organizations. This article reports on the current configuration of international academic programs in nonprofit and philanthropic studies in Africa, Asia, the Pacific, Europe, and the Americas (apart from the United States), describing the various forms of education and training programs from country to country and continent to continent. The authors examine the similarities and differences in nonprofit management education programs in different parts of the world, seeking to explain why education programs have a range of forms indifferent parts of the world, according to different historical, institutional, and cultural contexts, thus furthering understanding of the asymmetries and complexities of existing NPO and NGO education and training programs.

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 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)0.000
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 score0.360
Threshold uncertainty score0.595

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.040
GPT teacher head0.309
Teacher spread0.269 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations50
Published2007
Admission routes1
Has abstractyes

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