Bibliographic record
Abstract
Being hopeful and confident about the future in a northern town like Churchill, Manitoba, is hard.It has a long colonial history that is spatially evident in the designed physical environment.Much infrastructure is outdated, reflecting outdated needs.The Town's small population struggles with issues that many small Canadian towns face, including changing industries, resulting in unemployment, inadequate health care, an ageing population, and a desire to exploit tourism.With climate change, Churchill is at risk of further isolation as the rail line, which brings passengers and supplies in and out, travels over permafrost, which is melting.First-time visitors often suffer from southern and urban bias, making it challenging to find optimism in the Town.Most visitors quickly move on to tourist operations beyond town limits.This studio focused on the Town of Churchill, Manitoba, through a lens of optimism, with an emphasis placed on the challenges of relevant, equitable environmental and social design in the post-industrial world.Landscape designers act within networks of human and non-human assemblages.We work to enable others to experience and enjoy beauty.And in education, we work to help students see beauty.Is optimism seeing beauty?What is the emergent future for this settlement, following the global pandemic, following years of decline, following all the implications of climate change?Can we understand rural, small-town Canada through the thinking of urban infrastructure?Is Churchill rural, small-town Canada, or is Churchill other?What works and what doesn't work?Should landscape designers consider rural?Today, our society at large often sees ruralism as a stage prior to urbanism, or as an obsolete or derelict piece of the past.However, rural areas play a vital role in our future and our notions of progress.As designers concerned with sustaining and regenerating the diversity of our cultural and ecological landscapes, we must come to terms with our urban bias and begin to ask ourselves: what is rural landscape architecture and what are its methodologies?What does a rural landscape architecture framework look like and how do we begin to mold, grow, and employ one?1 -Lindsay BurnetteWith a focus on rural communities and infrastructure in northern places, this studio asked: how can optimism guide students of Landscape + Urbanism in an environmental design program to offer hope through well-designed exterior space in this isolated community on the coast of Hudson Bay? Can optimism be found in the land itself?In the community?What
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 0.004 |
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".