Reading and understanding the built environment of Quebec (Canada): the result of thirty years of research
Bibliographic record
Abstract
This paper discusses the state of research on the physical and spatial organization of villages, cities, and agglomerations of the territory of the province of Quebec, Canada. In addition to presenting the key notions of urban morphology opening the way to an in- depth understanding of the mode of structuring of human settlements, the paper uses several case studies to present the methods of analysis used by this field of research to read and characterize the built environments of Quebec. Some of these examples show how morphological knowledge has been developed at multiple scales to contribute to the development of the field. A body of research carried out over more than 30 years presents the first divisions of the territory, the birth of the first towns and villages, the formation of cities and of their outskirts, until the emergence of the first suburbs. Therefore, the paper presents the development of the local research tradition of urban morphology in Quebec, which evolved throughout the 1990s, strongly connected to European morphological schools. The theoretical concepts and the methods aim to offer useful lessons to feed the work of planning, design, and management of the quality and integrity of the built environment of Quebec.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".