Bibliographic record
Abstract
This thesis examines the feasibility of fostering “sustainable foodscapes” in urban communities. A review of the literature on the topics of sustainability, resilience, sustainable food security, and healthy communities is used to determine to the definition of “sustainable foodscapes.” This thesis uses a framework of socio-ecological restoration to consider how communities might adopt sustainable foodscapes. A case study is conducted in the city of Waterloo, Ontario to test the criteria of sustainable foodscapes and explore some of the practical opportunities and barriers to developing sustainable foodscapes in an urban community. \nThe methods for the case study include semi-structured interviews. Interview results indicate that a variety of sustainable foodscapes such as community gardening, individual gardening, and foraging are used in Waterloo already, and survey results suggest that various members of the community are open to the adoption of these foodscapes. The case study results reveal that diverse community members view sustainable foodscapes as an important contribution to community health, less for the purpose of ecological sustainability than for their usefulness as a way of promoting community interaction, social learning, and fostering a sense of place. Ways to conduct a socio-ecological restoration for sustainable foodscapes in Waterloo could include increasing areas for the purposes of foraging to occur in an ecologically benign manner, such as on marginal or private land; creating municipal policies and Official Plans that provide support for community gardens, and fostering more accepting attitudes towards sustainable foodscapes by providing increased opportunities for education and participation among community members.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".