Beneath the pines: rebuilding the natural image of Sudbury through alternative residential development strategies.
Bibliographic record
Abstract
Sudbury Ontario has been host to to innumerable \nextraordinary events, both natural and man-made, \nthat have created polarizing perspectives of the \nnatural. The relatively short 140-year history of \nwhite settlement in the Sudbury area at the hands of \nindustrialists has seen the scarring of a unique and \ngeologically rich landscape composed over billions \nof years which has contributed to an uncertain \nimage of the city, a loss of northern identity, and \nmore generally, an image that does not reflect the \nspirit of the place of the people that reside inhabit \nthe area. More recently, the suburbanization of \nSudbury has further alienated the concept place \nand identity through the quick development of \nsub-divisions complete with foreign concepts \nof living in manufactured landscapes. The thesis \nexplores the relatively short history of settlement \nwithin the natural landscapes of Sudbury and how \nit can inform strategies for the slow development \nof architecture that is distinctly of the place and of \nthe people. The study of the morphology around \nRamsey Lake informs new strategies for the public \nstewardships of important natural contributors to \nthe image of the city. The thesis questions how \nalternative strategies for community building united by strong and formalized position of stewardship, \ncan be formalized into new architectural typologies \nthat contribute to the natural image of the city. \nUltimately, a scarred image of the city has emerged \nand as the mining industry slows, and arguments \nagainst suburbanization strengthen, the thesis \nexplores how individuals can reclaim a sense of \nownership in the development of the image of \nthe city through thoughtfully considered individual \ndwellings united by a collective focus of sensitive \ngrowth in tune with that of the natural landscape. \nSince the permanent settlements in the Sudbury \narea, industrialization have damaged the natural \nimage of the city to the point where descriptions \nand pictures of the ancient landscapes only live in \nthe imagination. This is a story of understanding \nthe humans place within the timeline of history and \nserves to create new perspective of development \nin the naturally rich landscapes of Sudbury. There \nis a beauty of the Sudbury landscape, a beauty \nworth preserving through the oil paints of the \nGroup of Seven that lies under scars of industry.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.020 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 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".