Public Perception of “Who is a Volunteer”: An Examination of the Net-Cost Approach from a Cross-Cultural Perspective
Bibliographic record
Abstract
Our aim is to enhance the knowledge regarding how the public assess and rate volunteerism. We begin by first developing the model for understanding the potential use of the net-cost concept in eliciting the public’s subjective perceptions on the extent to which certain activities are perceived as volunteerism. Four hypotheses relevant to the use of the net-cost concept are developed. We developed a questionnaire consisting of 50 case scenarios and applied it in Canada, India, Italy, Netherlands, and Georgia and Philadelphia in the United States, each with a sample of 450 adults or more. With one exception, our net-cost hypotheses are supported, suggesting that the public perception of volunteering is strongly linked with the costs and benefits that accrue to the individual from the volunteering activity, and that this result holds true across different cultures. Finally, we suggest directions for future research that can shed further light on the relationship between net cost and public good.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".