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Record W2044221109 · doi:10.1177/0146167209359699

The Stingy Hour: How Accounting for Time Affects Volunteering

2010· article· en· W2044221109 on OpenAlexaff
Sanford E. DeVoe, Jeffrey Pfeffer

Bibliographic record

VenuePersonality and Social Psychology Bulletin · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsVolunteerPsychologySocial psychologyWorkforceValue (mathematics)Volunteer workPublic relationsLawPolitical science

Abstract

fetched live from OpenAlex

These studies examined how the practice of accounting for one's time-so that work can be billed or charged to specific clients or projects-affects the decision to allocate time to volunteer activities. Using longitudinal data collected from law students transitioning to their first jobs, Study 1 showed that exposure to billing time diminished individuals' willingness to volunteer, even after controlling for attitudes about volunteering held before entering the workforce as well as the individual's specific opportunity costs of volunteering time. Studies 2-5 experimentally manipulated billing time and confirmed its causal effect on individuals' willingness to volunteer and actual volunteering behavior. Study 5 showed that the effect of exposure to billing time on volunteering occurred above and beyond any effects on general self-efficacy or self-determination. Individual differences moderated the effects of billing, such that people who did not value money as much were less affected.

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)0.003
metaresearch head score (Gemma)0.021
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 score0.003
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.027
GPT teacher head0.332
Teacher spread0.305 · 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

Citations51
Published2010
Admission routes1
Has abstractyes

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