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Record W2087899766 · doi:10.1002/nml.221

Measuring social values in philanthropic foundations

2009· article· en· W2087899766 on OpenAlexaboutno aff
John R. Whitman

Bibliographic record

VenueNonprofit Management and Leadership · 2009
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsnot available
Fundersnot available
KeywordsVisionConsistency (knowledge bases)Identification (biology)Foundation (evidence)SociologyResource (disambiguation)VocabularySample (material)ChartSocial sciencePolitical scienceComputer scienceLinguisticsLawStatistics

Abstract

fetched live from OpenAlex

Abstract Philanthropic foundations are seen as organizations that allocate resources to achieve their visions of a better world. Drawing on a sample of foundations in Canada, the United States, and Europe, this research undertakes to reveal the social values that constitute such visions and to measure the consistency between espoused social values and those conveyed by resource allocations. A social values identification and measurement instrument is described and tested. The social values that comprise the instrument are presented in a chart of social values. A methodology for measuring the consistency between social values espoused by a foundation and those actually conveyed by resource allocation decisions is described, tested, and critiqued. It is argued that the results of this research provide a basis on which to pursue development of a standardized vocabulary of social values that may enhance understanding and discourse regarding the purposes and work of foundations, as well as provide a basis for cross‐cultural comparative analyses of foundations.

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)0.013
metaresearch head score (Gemma)0.041
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.013
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.041
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.003
Science and technology studies0.0030.006
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0000.001
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 head0.446
GPT teacher head0.386
Teacher spread0.060 · 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 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

Citations37
Published2009
Admission routes1
Has abstractyes

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