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Record W1977881456 · doi:10.1111/1536-7150.00065

A City Divided by Political Philosophies: Residential Development in a Bi‐Provincial City in Canada

2001· article· en· W1977881456 on OpenAlexaffabout
Gura Bhargava

Bibliographic record

VenueAmerican Journal of Economics and Sociology · 2001
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsIdeologyPoliticsPublic economicsEconomic growthEconomicsBusinessPolitical science

Abstract

fetched live from OpenAlex

This paper examines the impact of two distinct political ideologies on the development of residential dwellings, particularly single‐family residences, in Lloydminster, a bi‐provincial city in Canada. Lloydminster is a city of about 22,000 which straddles the provinces of Alberta and Saskatchewan. The two provinces are recognized for the dissimilarities in their political orientations. These dissimilarities are reflected in different taxation policies, different Medicare and health insurance policies and different auto insurance policies, which have given rise to a quantitative and qualitative split in the market for single‐family residences in Lloydminster. This duality of the housing market is manifested in Saskatchewan's “affordability” and Alberta's “desirability.” Alberta's tax policies have attracted “place entrepreneurs” to build upscale residences on its side for homeowners in high income brackets, who benefit from its tax policies. Saskatchewan's health care and auto insurance advantages have attracted those with such priorities and with moderate incomes. It is contended here that while distinct policies may have contributed to an uneven bi‐provincial housing market, the disparity between the markets has been sustained and reinforced by all players who construct the housing reality through myths and perceptions as well as interpretations of policies and their underlying ideologies.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.109
Threshold uncertainty score0.736

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.026
GPT teacher head0.223
Teacher spread0.197 · 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 teacher head, 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

Citations1
Published2001
Admission routes2
Has abstractyes

Explore more

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