Exploring the Complexity of Community Gardens: A North Bay, Ontario Case Study
Bibliographic record
Abstract
While community gardens are often common feature in cities across North America, academic and public discourses on their conceptualizations remains ambiguous (Guitart, Pickering & Bryne 2012). Some critical research on community gardens examines the academic consequences of conceptual ambiguity, but there is little focus on the practical implications. The purpose of this research is to examine grassroots interpretations of community gardens and groups and the implications of diverging understandings and experiences on the work of supportive non-governmental organizations. Ethnographic fieldwork was conducted in North Bay, Ontario with data collected through participant observation, semi-structured interviews and participatory mapping focus groups with various community garden actors. This thesis demonstrates how two gardens and one gardening group are interpreted as different forms of urban agriculture, including community gardens, through the framework of political ecology. As the goal of this project is to provide the North Bay Community Garden Coalition with recommendations for strengthening their role in supporting and promoting community gardening initiatives in North Bay, I conclude my thesis by exploring the ways in which ‘community garden’ diversity impacts their mandate and by offering suggestions that reflect the context of North Bay.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.030 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".