Lessons from the land: how Northern Ontario architecture can be Influenced by land-based Learning
Bibliographic record
Abstract
Health and well-being are a relationship that can be explored through \nthe Land. Understanding this connection is something the Indigenous peoples living in Canada have been doing for thousands of years. \nAnalyzing the social determinants of health in urban and rural \ncommunities, the work conducted tries to rekindle ancestral knowledge \nto create a strong relation to the Land using a series of reciprocal connections with various members of the community of Sudbury. Utilizing three \nmain stakeholders: Conservation Sudbury, professionals at Laurentian \nUniversity, and Indigenous scholars, the process undertook a bottom-up \napproach to design that fulfils as much of the community’s needs and \nwants as possible. Located on the Lake Laurentian Conservation Area, \nthe proposed project aims to bring small-scale architectural interventions \nalong various trail segments to encourage experiential education and \nLand-based learning to ultimately increase peoples’ health and well-being.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.016 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".