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Record W2068009412 · doi:10.1007/s11266-014-9496-4

Environmental Philanthropy and Environmental Behavior in Five Countries: Is There Convergence Among Youth?

2014· article· en· W2068009412 on OpenAlexaboutno aff
Tally Katz‐Gerro, Itay Greenspan, Femida Handy, Hoon-Young Lee, Andreas Frey

Bibliographic record

VenueVOLUNTAS International Journal of Voluntary and Nonprofit Organizations · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsConvergence (economics)Test (biology)Environmental changePolitical sciencePsychologyEconomic growthEcologyEconomicsClimate change

Abstract

fetched live from OpenAlex

Abstract This paper compares and contrasts environmental philanthropy, environmental behavior, and their determinants among university students in five countries: Canada, Germany, Israel, South Korea, and the United States. The paper’s unique contribution to the nonprofit literature is its focus on environmental philanthropy as an unexplored form of philanthropic behavior, and the ability to test environmental philanthropy in an international setting and in comparison to other modes of environmental behavior. By environmental philanthropy, we mean giving to, and volunteering in, various environmental non-governmental organizations, and by environmental behavior, we refer to daily behaviors in the private sphere with ecological implications. We hypothesize that although the five countries vary on several characteristics, the student populations—who are young, educated, and exposed to global ideas and norms—will be relatively similar to each other in their environmental and philanthropic behavior and in the determinants of such behavior. To test this hypothesis, a standardized questionnaire was administered to 8,477 students on five campuses. Results show significant differences between students in their environmental philanthropic behavior, as well as in the demographic and attitudinal determinants of such behaviors.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.004
GPT teacher head0.219
Teacher spread0.216 · 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.

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

Citations42
Published2014
Admission routes1
Has abstractyes

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