What makes a convivial community tool? Investigating grassroots ecological restoration
Bibliographic record
Abstract
The practice of ecological restoration through native plant gardening is emerging among community groups as a means of addressing degradation in urban landscapes. Despite this trend, restoration remains primarily studied as a professional practice. Grassroots associations support people in growing native plants, but within the research on restoration ecology, there remains little study of how non-professionals engage in the practice. We adapt and expand Ivan Illich’s concept of a convivial community tool (i.e., a tool that is open and accessible rather than restricted to certain users) to ecological restoration through a case study of the Ottawa Wildflower Seed Library. Participants highlighted two main strategies of the seed library: overcoming barriers and supporting emergent practices. The seed library helped people overcome the barriers of plant availability, cost, and knowledge, while supporting spontaneous initiatives from volunteers to further the mission of the seed library. We argue that these two strategies operationalize the idea of a convivial community tool. This research contributes an understanding of one way that ecological restoration can broaden its appeal by empowering non-professionals to engage in restoration and provides a starting point for a novel organizational framework based on Illich’s ideas.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.032 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".