Bibliographic record
Abstract
Over the summer of 2015, the Youth Collaborative for Chinatown worked to activate public spaces in Vancouver’s Chinatown in an intergenerational, intercultural manner through a series of events: the “Hot and Noisy” (熱鬧) Chinatown Mahjong Socials. On the surface, our goal was simple: to bring back Chinatown’s 熱鬧, or “yeet low” in Cantonese, – literally, “hot and noisy”, or liveliness and energy. Below the surface, we had more complex goals of being able to bring a youth voice to planning processes about the future of Chinatown, and building up political and social capacity of young generations of Chinese Canadians. We decided that our approach to activating public space had several criteria. It needed to be visible. It needed to be collaborative. It needed to demonstrate a cohesive, coordinated effort undertaken by younger generations, with the ability to involve many others. It needed to be intercultural and multilingual. It needed to foster relationships between young and old. It needed to be feasible to implement within a very short time frame. It needed to involve no to low hard costs. And it needed to be possible with the resources and skills we could readily bring to the table, amongst our team of organizers. By temporarily activating a public space, there is an opportunity to both share and transform the stories that we tell ourselves and each other in relation to it, and to create spaces of belonging. Based on participant observation/action as a member of the Youth Collaborative for Chinatown, I describe the “Hot and Noisy” (熱鬧) Chinatown Mahjong Socials as a case study of a youth-driven, grassroots process in public space activation. I discuss lessons learned and the implications for planning, urban design and community organizing.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".