Rat Routes to Berried Treasure: Recipes as Alternate Narratives of Urban Agriculture
Bibliographic record
Abstract
This thesis considers the constitution of urban places, futures and belonging through foraging, urban gardening and recipe creation. Adopting a community-based research approach, the research embraces values of inclusion and is an active attempt to diversify the ways in which urban greenspaces are planned and imagined. At the heart of this research is the co-creation of a recipe book by a group of culinarily-inclined English language learners and a Métis instructor from Edmonton who is the author of this thesis. This thesis considered how participation in a community urban agricultural and recipe project could increase belonging as well as aid in building more inclusive futures for its participants and for the City of Edmonton. The recipe book created in this project is the culmination of a year of urban gardening and foraging within the City of Edmonton, Alberta; of experiencing and reflecting upon the ways in which we relate to the land, and our imaginations for our future. Our recipes-as-stories offer a window into who and where we are, and specifically on green and wild city spaces where planning, policies and histories do not always include our voices as women, mothers, immigrants, and Indigenous people. As urban food spaces continue to rise in prominence in cities such as Edmonton, how these spaces are defined, managed, who has access to them, and whose voices and experiences are underrepresented in the planning and use of such spaces are essential questions. Through the narratives, experiences, and reflections that we gathered as we created place-based recipes, we carved out spaces of belonging and agency for ourselves and presented our own imaginary of urban agriculture. This imaginary draws from the magic of the everyday to visualize an inclusive and sustainable future in our City. Our vision and our recipes-as-practice reach beyond the generally understood boundaries of urban agriculture and include not only current and local gardens, but also global places, urban forests and intergenerational connections.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.033 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| 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".