Bibliographic record
Abstract
Vancouver’s downtown peninsula symbolically describes the sense of place unique to the city as a whole. It is a livable city with a \nstrong connection to its natural surroundings, witnessed in its very active population. This sense of place, however, has far more to do with its relationships to its natural setting, the mountains and ocean, than its urban spaces or architecture. Most of the central public spaces are quite ordinary. Although the temperate climate \nis ideal for inhabiting streets and squares, the majority of the city’s prominent public spaces exist along the water’s edge. Ultimately locals and visitors gravitate to the periphery and the nearby wilderness, conditioning them to look outward on the natural setting as opposed to reflecting inward on the city. Vancouver’s iconic identity exists primarily on the panoramic level. Great cities throughout the world exist without the splendour of mountains \nand ocean and Vancouver must stop relying on these to constitute its important public spaces. \nThis thesis makes a proposal for a series of large scale urban interventions on the downtown peninsula that serve to augment \nVancouver’s sense of place. The first intervention will replace unnecessary car space with public space, in order to incrementally create, over a number of years, an extensive pedestrian network that links its public spaces. This will incorporate characteristics of successful urban systems found in Barcelona, Bogota, Copenhagen, Curitiba and Portland, treating the street not just as a transportation corridor but \nalso as a public space, and a democratic forum. The second intervention will remove many low to mid-density ‘underperforming’ residential buildings, creating a diagonal pedestrian and transit boulevard that bisects the downtown peninsula, linking major public spaces such as English Bay Beach, Robson Square, and Waterfront Station. Along this diagonal, new high density mixed-use development will offer an increased number of residential, commercial and cultural facilities. The new public spaces and developments created by the proposed \ndiagonal boulevard will provide Vancouver with a civic realm better connected than it has ever been. Vancouver will become a city of great pedestrian public spaces, strongly linked to natural surroundings that serve an active and environmentally conscious population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".