Bibliographic record
Abstract
Abstract The volunteer management literature suggests that the most effective means of recruitment is personal asking. However, agencies that apply this method do not report the expected success in volunteer recruitment. Often they face the volunteer recruitment fallacy: those people assumed to be interested in volunteering do not necessarily volunteer. Based on the literature of shyness or social anxiety and on empirical observations, this article suggests that social anxiety often deters volunteering by new recruits. We hypothesize that people with greater levels of social anxiety will be less likely to volunteer. Furthermore, we hypothesize that people with high social anxiety will prefer to give monetary support to worthy causes rather than volunteer their time, and if they do choose to volunteer, they will do so alongside friends. Our hypotheses are supported based on the findings from a large‐scale nonrandom sample in North America. We suggest how to avoid the volunteer recruitment fallacy by creating a personal environment in which high‐social‐anxiety recruits feel safe and accepted. By removing the fear of being negatively judged by strangers as they enter the agency and creating a more personal approach, new recruits may have a higher probability of becoming long‐term and consistent volunteers.
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 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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".