Bibliographic record
Abstract
The 31st largest city in North America, 4th in Canada, home to nearly 1,000,000 residents, Ottawa is a town in need of change. It can ill afford the financial burden and social impacts of its urban sprawl, and its traditional economic engine, the Canadian Government, no longer offers enough civil service jobs or incentives to the private sector to sustain the livelihood of its inhabitants. This paper deals with intensification, a policy introduced by Ontario in its Provincial Policy Statement (2005: http://www.mah.gov.on.ca/Page1485.aspx; revised 2014: http://www.mah.gov.on.ca/Page10679.aspx) to counter urban sprawl, and suggests that, by giving priority to one of its components — the densification of arterial main streets — the municipal council may have stumbled on a way both to slow down the growth of suburbia and to make Ottawa more attractive to the people most likely to generate new ideas, new technologies and new jobs. The paper also explains how local community associations from the city’s 9th ward have, from the outset, prompted city councillors and officials to envisage main street densification along Merivale Road as a strategy contributing to Ottawa’s regeneration and renewal.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.031 | 0.010 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".