Ground work: revitalizing Hamilton through urban agriculture
Bibliographic record
Abstract
The landscapes of cities around the Great Lakes Region grapple with the far-reaching \nconsequences of intense industrial exploitation. The environment’s capacity to sustain \nlife and contribute to all species health and well-being is damaged. In addition to \nintense environmental pollution, the industrial manufacturing boom and bust cycles \nengendered waves of job loss, which plunged already disadvantaged workers into \npoverty. Neighbourhoods filling the spaces in-between factories once home to workers \nface greater risks of disease and poverty as they sit in this undesired landscape. \nSustainable rehabilitation of the post-industrial landscape and its remaining architectural \nfragments must be pursued carefully to mend the trauma of industrial manufacturing \nthat contaminated deep into the soil and outward into the bodies of the community. This \ncondition creates social and spatial inequalities that literally and metaphorically grow out \nof the damaged ground. This thesis project takes the position that architecture and urban \ndesign have essential roles in combating socio-economic and spatial inequalities and \nadopts a “ground-up” methodology, which is construed and mobilized in various ways. \nFocusing on Hamilton, Ontario, which sits at one of the most contaminated points along \nLake Ontario yet whose urban centre sits atop the once fertile extension of the Niagara \nfruit basket, this thesis project explores how permaculture could resuscitate the valuable \nsoil that lies deep in the ground and support the surrounding community. Advancing a \nholistic vision for environmentally and culturally sustainable design, the remediation of \nthe land to support sustainable food production is at the heart of a design approach to \ntackling industrial contamination and its many long-lasting effects. Following the first stages of rebuilding the ground, the design intervention in the Kieth neighbourhood \nadjacent to Hamilton’s industrial port comprises a hybrid program that includes an urban \nagriculture hub with a market, stores for locally fabricated goods, and a restaurant - cafe \nto showcase the produce grown on-site. These commercial operations will be linked to a \ncohousing neighbourhood development centred around communal spaces, surrounded \nby a landscape remediated through phases of planting to be used for food production, all \nof which work together to cultivate the well-being of residents. This thesis project aims to \nheal former industrial lands in Hamilton to address food (in)security by transforming a toxic \nlandscape into an accessible neighbourhood within a productive, habitable environment.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".