Shaping Vancouver Series 2019: Reshaping Local Places
Bibliographic record
Abstract
Under many different names, including “revitalization”and “regeneration”, heritage is and can be used to craft a positive place image, develop local economic sectors, create a neighbourhood centre for culture, and improve upon the animation of local areas. This change can be compelling, but also has its challenges.\n\nThis process is especially relevant and timely in the False Creek Flats, Chinatown, and Punjabi Market areas of Vancouver. Under the False Creek Flats Plan approved in 2017, the approach is to make the area a thriving and innovative economic zone which “builds off of existing character… by leveraging key character assets, histories and economic anchors”. Chinatown is looking towards a process (including application for World Heritage) where a heritage informed by experiential authenticity, culture and ordinary daily life forms the basis for social and economic revitalization. Punjabi Market, while not having undergone extensive planning exercises, desires a future where the three block district is a place filled with Punjabi experiences for people to enjoy.\n\nIn this first talk, we look at how heritage can be used to reshape these places in the city. In particular, we examine:\n\nWhat roles do these areas play in the broader narrative of the city, and how do we plan for them?\n\nWhat is the difference between taking a “landscape” view of heritage vs a “site” view of heritage?\n\nHow can heritage be used as a lens through which to view issues around the integration of large developments, such as St. Paul’s Hospital, with neighbouring landscapes and the established ways of existing that are unique to people, organizations and businesses in these areas.\nWhat conflicts emerge between the various meanings and values given to places?\n\nHow can culture-led plans fulfill economic, social and cultural objectives set for areas such as Chinatown and Punjabi Market?\n
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.008 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.047 | 0.007 |
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".