Ground work: revitalizing Hamilton through urban agriculture
Bibliographic record
Abstract
The landscapes of cities around the Great Lakes Region grapple with the far-reaching consequences of intense industrial exploitation. The environment’s capacity to sustain life and contribute to all species health and well-being is damaged. In addition to intense environmental pollution, the industrial manufacturing boom and bust cycles engendered waves of job loss, which plunged already disadvantaged workers into poverty. Neighbourhoods filling the spaces in-between factories once home to workers face greater risks of disease and poverty as they sit in this undesired landscape. Sustainable rehabilitation of the post-industrial landscape and its remaining architectural fragments must be pursued carefully to mend the trauma of industrial manufacturing that contaminated deep into the soil and outward into the bodies of the community. This condition creates social and spatial inequalities that literally and metaphorically grow out of the damaged ground. This thesis project takes the position that architecture and urban design have essential roles in combating socio-economic and spatial inequalities and adopts a “ground-up” methodology, which is construed and mobilized in various ways. Focusing on Hamilton, Ontario, which sits at one of the most contaminated points along Lake Ontario yet whose urban centre sits atop the once fertile extension of the Niagara fruit basket, this thesis project explores how permaculture could resuscitate the valuable soil that lies deep in the ground and support the surrounding community. Advancing a holistic vision for environmentally and culturally sustainable design, the remediation of the land to support sustainable food production is at the heart of a design approach to tackling industrial contamination and its many long-lasting effects. Following the first stages of rebuilding the ground, the design intervention in the Kieth neighbourhood adjacent to Hamilton’s industrial port comprises a hybrid program that includes an urban agriculture hub with a market, stores for locally fabricated goods, and a restaurant - cafe to showcase the produce grown on-site. These commercial operations will be linked to a cohousing neighbourhood development centred around communal spaces, surrounded by a landscape remediated through phases of planting to be used for food production, all of which work together to cultivate the well-being of residents. This thesis project aims to heal former industrial lands in Hamilton to address food (in)security by transforming a toxic landscape into an accessible neighbourhood within a productive, habitable environment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.031 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".