Bibliographic record
Abstract
After a very important loss of the population that used to live in 1970 and 1971 in the cores of the cities of Montreal and Mexico, this article, based on the 1990(1)-2000(1) urban development trends shown in the Canadian and Mexican official censuses, estimates through an arithmetic linear extrapolation equation, which one will be the most likely urban attraction power that those cores composed by a set of old districts, will exert on the population to the 2025, 2026 planning thresholds. In this context, this study analyses the re-densification expectations that both the Municipality of Montreal (i.e., the City of Montreal) and the Municipality of Cuauhtémoc (i.e., the Delegación Política Cuauhtemoc) have, to attempt recovering the 1970, 1971 original populations, to the indicated years. The corresponding arithmetic estimations made on the male, female, senior +65, children 0-14, married, and single populations considered as representatives to compose the future demographic profiles on the analyzed areas, found in the Canadian case that all the populations to the 2026 year, will be around the 50-90% recovery range of the 1971 original base, while in the Mexican case, the equivalent populations will oscillate to the 2025 year, around the 30-80% recovery range of the 1970 original base. This study concludes that Montreal core’s attraction power will be much more significant than the equivalent in Cuauhtémoc, explaining this urban phenomenon in terms of the 1990(1)-2000(1) job and housing creation trends on those areas.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".