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Record W3125794735

When Does Recognition Increase Charitable Behavior? Toward a Moral Identity-Based Model

2013· article· en· W3125794735 on OpenAlexaff
Karen Page Winterich, Vikas Mittal, Karl Aquino

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsInternalizationIdentity (music)DonationSocial psychologyPsychologyMoral behaviorMoral disengagementPolitical scienceLawMedicine
DOInot available

Abstract

fetched live from OpenAlex

Each year, individuals in the United States donate over $200 billion to charitable causes. To motivate donors, charities offer donors recognition, even though it is not well understood if and how recognition increases charitable behavior. This research focuses on how the effectiveness of recognition on charitable behavior is dependent on the joint influence of two distinct dimensions of moral identity — internalization and symbolization (Aquino and Reed 2002). Three studies examining both monetary donations and volunteering behavior show recognition increases charitable behavior among those characterized by high moral identity symbolization and low moral identity internalization. Interestingly, those who are high in moral identity internalization are uninfluenced by recognition. By understanding correlates of the two dimensions of moral identity among its donor base, nonprofits can strategically recognize potential donors to maximize donation and volunteering behavior.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.706
Threshold uncertainty score0.999

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.073
GPT teacher head0.313
Teacher spread0.240 · 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

Citations5
Published2013
Admission routes1
Has abstractyes

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