Volunteering Trajectories and the COVID-19 Pandemic: Persistent, Emergent, and Former Volunteers and Personal, Moral, and Prudential Reasoning
Bibliographic record
Abstract
Although trajectories of youth volunteering were disrupted by the COVID-19 pandemic, nevertheless some youth persisted in volunteering, and others emerged as volunteers. To understand volunteering trajectories, the present mixed method study proposed a model adapted from prior literature and examined volunteer trajectories during the pandemic. Youths’ volunteer trajectories were categorized (as persistent, emergent, or former volunteer, or persistent non-volunteer), and their justifications for their volunteer decisions were classified using social domain theory (personal, social, moral, and prudential). A sample of 461 youth ( M age = 19.26; 68.8% female; 41.6% European or North American) from a large Canadian university completed a retrospective survey on pandemic volunteering and volunteer decisions. Volunteer decisions were coded using conventional and directed qualitative content analysis. Although the pandemic disrupted the volunteering trajectories of former volunteers, overall, more youth persisted or emerged as volunteers during the pandemic, a finding framed in both the trajectory and emergency and disaster literature. Volunteers were more likely to use moral justifications, whereas prudential justifications were more frequent among non-volunteers. The present study offers insight into the impact of the pandemic on youth volunteering and is one of the first studies to find a substantive role for prudential reasoning in youth decision making.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".