Volunteering for nature: Motivations for participating in a biodiversity conservation volunteer program
Bibliographic record
Abstract
Understanding volunteer motivations for participating in nature conservation programs is an important element in the design and provision of programs intended to harness the increasingly important talents and labour that volunteers bring to conservation programs. The purpose of this paper is to highlight the motivations of participants in Volunteer for Nature, an Ontario-based nature conservation program. The study was framed within a social psychology theoretical perspective and qualitative methods were used. The participants were female and male volunteers ranging in age from 17 to 63 years old. Key motives for participating in the volunteer conservation vacation program included: 1) pleasure seeking, 2) program “perks,” 3) “place” and nature-based context, 4) leaving a legacy, and 5) altruism. The study reinforces much of the theoretical literature already existing on volunteers including volunteering as a leisure activity and motives associated with volunteering. However two unique points are explored: 1) the distinctive nature-based volunteering context, and 2) the “value-added” nature of volunteering vacations. Further, conceptual linkages with concepts such as serious leisure are discussed. An increased understanding of volunteer tourists who participate in nature conservation programs is the greatest contribution of this study.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".