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Record W4386471094 · doi:10.32920/24085563.v1

A spatial analysis of the geodemographic segmentation system in the Greater Toronto area

2023· preprint· en· W4386471094 on OpenAlexaffabout
Anwar Abushammala

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsToronto Metropolitan UniversityStatistics Canada
Fundersnot available
KeywordsGeographyPovertyCensusCluster (spacecraft)Distribution (mathematics)PopulationSegmentationCartographyHousehold incomeSocioeconomicsRegional scienceEconomic growthDemographyEconomicsSociologyComputer science

Abstract

fetched live from OpenAlex

The Greater Toronto Area is one of the most iconic areas in Canada for dense population, multiculturalism, wide selection of job opportunity and diverse neighborhood. Studying the consumer behavior of the GTA adds much value and interest to the public and private sectors. Previous studies and research forces on the wealth of the area and neglects to study the spatial poverty distribution of the area. One way to have a better understanding of the spatial poverty distribution of the geodemographic segmentation is by using census and expenditure data. The spatial poverty distribution geodemographic segmentation system generated five clusters. The poorest two clusters generated are Impoverished Black Single Parents and Chinese Widow Corridors. The Impoverished Black Single Parents has an annual average household income of $66,038. The Chinese Widow Corridors has an annual average household income of $84,066. Using summary tables, indices and maps better shows the different characteristics of each cluster.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.010
Science and technology studies0.0010.001
Scholarly communication0.0020.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.028
GPT teacher head0.232
Teacher spread0.204 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2023
Admission routes2
Has abstractyes

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