mind.heart.mouth \nCare and Community through Collective Gardening
Bibliographic record
Abstract
Over two years, from 2018 to 2020, I ran a sequence of workshops and initiated a collective garden on the Loyola campus of Concordia University in Montréal. Using research-creation, in the form of a garden, I explored how embodied knowledge can inform experiential learning, care, and sensory experiences. This garden was a site of intergenerational knowledge exchange involving seniors and students. This thesis addresses the interconnected areas of environmental education, embodied cultural experience, and food security. The central claim of this work is that gardening pedagogy providing sensory experiences, hands-on practices, and related prompts can stir reflections, challenge individual and cultural assumptions, and provide a space to foster and transform our sense of care and community. Furthermore, this project contributes to the field of environmental communication by moving away from traditional visual media to consider natural elements, such as dirt, seeds, seedlings, and vermicompost as devices of mediation that call upon all the senses to promote conscious engagement. It is also a small but creative intervention in the field of food security by offering a model of a university garden and companion workshop series that foster skills and build awareness.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.048 | 0.008 |
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".