MétaCan
Menu
Back to cohort

Espacios urbanos y su difícil redensificación

2013· article· es· W1570391379 on OpenAlexaboutno aff
Carlos Tello, Paul Lewis

Bibliographic record

VenueAmericanae (AECID Library) · 2013
Typearticle
Languagees
FieldSocial Sciences
TopicLatin American Urban Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGeography

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.366
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.003
Science and technology studies0.0010.005
Scholarly communication0.0010.003
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0140.006

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.

Opus teacher head0.010
GPT teacher head0.245
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueAmericanae (AECID Library)Same topicLatin American Urban StudiesFrench-language works237,207