Aboriginal Health Learning in the Forest and Cultivated Gardens: Building a Nutritious and Sustainable Food System
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Sustainable food systems are those in which diverse foods are produced in close proximity to a market. A dynamic, adaptive knowledge base that is grounded in local culture and geography and connected to outside knowledge resources is essential for such food systems to thrive. Sustainable food systems are particularly important to remote and Aboriginal communities, where extensive transportation makes food expensive and of poorer nutritional value. The Learning Garden program was developed and run with two First Nation communities in northwestern Ontario. With this program, the team adopted a holistic and experiential model of learning to begin rebuilding a knowledge base that would support a sustainable local food system. The program involved a series of workshops held in each community and facilitated by a community-based coordinator. Topics included cultivated gardening and forest foods. Results of survey data collected from 20 Aboriginal workshop participants are presented, revealing a moderate to low level of baseline knowledge of the traditional food system, and a reliance on the mainstream food system that is supported by food values that place convenience, ease, and price above the localness or cultural connectedness of the food. Preliminary findings from qualitative data are also presented on the process of learning that occurred in the program and some of the insights we have gained that are relevant to future adaptations of this program.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it