Connecting to nature through community engaged scholarship: Community gardens as sites for collaborative relationships, psychological, and physiological wellbeing
Bibliographic record
Abstract
Community gardens are recognized as being associated with a range of benefits for participants that include enhanced outcomes in physical and affective domains and community building. The purpose of this study was to research the impact of the New South Wales Royal Botanic Gardens (RBG) Community Greening (CG) program and to inform the ongoing development of this community outreach program. The organic community partnerships inherent in the design and the relationships between the Community Greening program participants and researchers is examined through the lens of Community Engaged Scholarship (CES). Over a seven-month period, the CG team implemented a community garden development program in six sites. Mixed-method research on the impact of the program found that the community gardening participants experienced positive changes in physical activity, psychological wellbeing and motivation for social engagement, and these outcomes were facilitated as a result of their relationships with members of the CG team. This paper examines how such programs, when explicitly framed as CES, could assist in consolidating nature-based community health and wellbeing programs and further legitimize community partnerships in development of community garden and green spaces as academically sound investigation and socio-economically justified activity. Expansion of this nature-based collaboration model may also enhance community engagement in green exercise, psychological wellbeing and community cohesion, and in turn support advocacy for greener environments locally, regionally and nationally.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".