Bibliographic record
Abstract
This project has gone through several phases over several years:The initial phase led to the development of the Healthy Rural Communities Toolkit (funded by Public Health Ontario). The second phase (funded by the Guelph/OMAFRA Partnership) involved KTT with workshops delivered across the Province. The third phase (funded by Public Health Ontario and the School of Environmental Design and Rural Development) saw the development of educational materials, most recently culminating in a graduate course oriented to planning and public health students. Overall, this project aims to identify evidence-informed strategies and models of practice for land use planning policies, procedures and designs for the built environment to improve population health outcomes in rural communities. It has been identified that these communities often have limited resources and minimal development. This presentation will introduce a main output of this project— a toolkit which identifies rural land use policies that have successfully increased the capacity of the community to achieve positive health outcomes. This toolkit is produced to advise public health professionals, land use planners, municipal staff and elected officials of effective strategies and models of practice. The presentation will also introduce educational materials developed in support of the graduate course: Healthy Rural and Small-Town Communities. This includes many publicly available videos and other resource materials.
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 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 | 0.000 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".