Developing a housing-based strategy towards downtown revitalisation : a case study of Winnipeg
Bibliographic record
Abstract
1.0 lrurRooucnoru 1.1 Preamble This thesis has been inspired by a lifelong interest in the nature of cities, their economies, and the communities within.As well, this thesis is motivated by a continued and sincere affection for this community and a commitment to promoting good public policy.The role of downtowns in North American cities has changed over the past few decades.Historically, downtowns were the centre of city life where citizens would work, shop, take advantage of social and cultural events, and pursue educational opportunities, among others.More recently, downtowns have struggled to find their place in a regional system and have not responded well to the many challenges they face.New technologies (most notably the automobile) have reduced the locational advantages of being downtown.Many functions that downtowns once performed are now found dispersed throughout suburban locations and even within the home.Downtowns have lost their relevance to a significant proportion of the population, as they are no longer regional destinations.The role of downtowns must be re-examined if their decline is to be halted.Unfortunately, while significant resources have been committed to address this decline, the results of previous downtown revitalisation initiatives have not resulted in a vibrant and appealing downtown, tending instead to reinforce the status quonamely, downtown as a commuter-oriented destination.Clearly, a new approach to downtown revitalisation is needed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".