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Record W1606685257 · doi:10.1002/nvsm.1435

Emerging philanthropy markets

2012· article· en· W1606685257 on OpenAlexaff
Richard Michon, Atul K. Tandon

Bibliographic record

VenueInternational Journal of Nonprofit and Voluntary Sector Marketing · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicCulture, Economy, and Development Studies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsProtestant work ethicCapitalismDeveloping countryPosition (finance)EconomicsValue (mathematics)ProtestantismEmerging marketsMarket economyPolitical scienceEconomic growthFinanceLaw

Abstract

fetched live from OpenAlex

Entrepreneurs are not the only ones to salivate at the call of emerging markets. Major nonprofit organizations that raise money in high‐income Organisation for Economic Co‐operation and Development countries for redistribution in developing countries are also looking for new sources of funds. Some of the countries that benefited from private philanthropy not too long ago are now in a position to help. The contribution of this paper is two‐prong. First, it introduces a robust market screening methodology to determine countries' capacities to give on the basis of macroeconomic and infrastructure indicators. Second, it identifies cross‐cultural predictors for charitable donations taken from the World Value Survey. A logistic regression calibrated on already successful fundraising operation scores countries on their propensity for private philanthropy. Research findings receive theoretical support from Max Weber's Protestant Ethic and the Spirit of Capitalism. Top countries for private philanthropy are of Protestant tradition and are at the origin of capitalism, as we know it today. Confucian countries that share a similar ethic and have opted for capitalist economies are first class candidates for private philanthropy. Copyright © 2012 John Wiley & Sons, Ltd.

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)0.003
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 score0.290
Threshold uncertainty score0.944

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.311
Teacher spread0.288 · 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

Citations9
Published2012
Admission routes1
Has abstractyes

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