Exploring community gardens as critical and complicated sites for adult education
Bibliographic record
Abstract
This research and practice chapter will describe and analyze the social and environmental relations that emerged from four separate adult education internships/community-based garden projects in Tio'tià:ke/Montreal. Methodologically and epistemologically, the author combines institutional ethnography and visual methodologies to explore and uncover how adult education internships in community gardens, gardening programming and greening can work to produce disproportionate outcomes for adult learners, educators, community workers and community members. In the process of creating, funding, enacting and evaluating adult education internships and many different gardens in community organizations and gentrifying neighbourhoods, this chapter will elucidate specific institutional contrivances (e.g., funding, policy, geographies of injustice, work processes, discourse, curricular) that are presently structuring and defining who experiences access to gardens, gardening and its ostensible health and well-being benefits, greenspaces, and adult environmental learning.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".