North End revitalization: landscape architecture as a means to building social capital
Bibliographic record
Abstract
As North American cities begin to age, our impermanent building methods inevitably lead to urban decay. Much of the housing stock has an expected lifespan and although this can be extended with proper and regular maintenance ultimately, much of this housing will face the bulldozer. Consequently, we are constantly engaged in the continuing cycle of urban revitalization, striving to keep our cities new and novel. This requires an immense amount of reinvestment and for some neighbourhoods, this is not an issue. This is not the case however for neighbourhoods considered to be âin distressâ which, face challenges not seen in more affluent areas. Revitalization efforts in at risk areas need to be more sensitive in their approach as they can displace, gentrify, and otherwise exacerbate the problems. This practicum is an exploration of that approach to renewal in grass roots fashion, using urban agriculture as a means to building community.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".