Environmental Volunteering: Motivations, Modes and Outcomes
Bibliographic record
Abstract
Volunteers play a key role in natural resource management: their commitment, time and labour constitute a major contribution towards managing environments in Australia and throughout the world. From the point of view of environmental managers much interest has focussed on defining tasks suitable to volunteers. However, we argue that an improved understanding of what motivates volunteers is required to sustain volunteer commitments to environmental management in the long term. This is particularly important given that multiple government programs rely heavily on volunteers in Australia, a phenomenon also noted in the UK, Canada, and the USA. Whilst there is considerable research on volunteering in other sectors (e.g. health), there has been relatively little attention paid to understanding environmental volunteering. Drawing on the literature from other sectors and environmental volunteering where available, we present a set of six broad motivations underpinning environmental volunteers and five different modes that environmental volunteering is manifested. We developed and refined the sets of motivations and modes through a pilot study involving interviews with volunteers and their coordinators from environmental groups in Sydney and the Bass Coast. The pilot study data emphasise the importance of promoting community education as a major focus of environmental volunteer groups and demonstrate concerns over the fine line between supporting and abusing volunteers given their role in delivering environmental outcomes.
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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 | 0.009 | 0.021 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".