Motivations for volunteers to participate in ecological restoration: a systematic map
Bibliographic record
Abstract
Volunteering is a central means by which communities become engaged in ecological restoration projects and understanding what motivates volunteers is a core preoccupation of researchers because it may help recruit more people. This study addresses the question: what are the motivations and barriers to participation in ecological restoration projects? The systematic literature map method was used to answer this question. The results revealed a typology of motivations that consists of 15 categories. A co‐occurrence network analysis of those categories revealed five core motivations that co‐occur most in the literature: having a positive environmental impact, acquiring and sharing knowledge, caring for the environment, social interactions and community, and human health and well‐being. Barriers to volunteering and the demographics of volunteers were also mapped in the literature, as they appeared frequently alongside motivations. The five core motivations represent a set of widely studied and well‐understood motivations which can inform the design of volunteer programs. The literature indexed by the systematic map can form the basis of further systematic reviews and meta‐analyses. This study highlights three major areas for future research: extrinsic motivations, demographics of volunteers who participate in ecological restoration, and project organization as a motivation.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".