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Record W2608901347

An explorative investigation of the dynamics of housing affordability in a booming oil economy: a case study of St. John's, Newfoundland 1991-2011

2016· dissertation· en· W2608901347 on OpenAlexfundaboutno aff
Aila Sinikka Okkola

Bibliographic record

VenueMemorial University Research Repository (Memorial University) · 2016
Typedissertation
Languageen
FieldDecision Sciences
Topicdemographic modeling and climate adaptation
Canadian institutionsnot available
FundersMemorial University of Newfoundland
KeywordsUrban agglomerationMetropolitan areaBoomEconomicsResource (disambiguation)Affordable housingCommodityBusinessEconomic geographyGeographyEconomic growthEngineeringFinance
DOInot available

Abstract

fetched live from OpenAlex

Researchers have addressed the dynamics of housing affordability in the major metropolitan areas in Canada. However, housing costs are also growing rapidly in smaller resource driven urban agglomerations during commodity booms. The objective of this thesis is to explore the dynamics of housing affordability in such urban agglomerations with a focus on St. John’s, Newfoundland. This project encompasses two sections. An exploratory section of the thesis presents a descriptive data analysis approach to the evolution of incomes and housing costs between 1991 and 2011, followed by an investigation of trends in housing cost to income ratio, homeownership rate, housing debt and housing quality. The second section employs a more rigorous methodology of quantile regression analysis to ascertain the relative importance of various household characteristics on housing cost to income burden. This thesis finds that new patterns of growing housing affordability problems are emerging in smaller resource driven urban agglomerations in Canada.

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)0.001
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 score0.027
Threshold uncertainty score0.180

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0040.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.093
GPT teacher head0.335
Teacher spread0.242 · 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
Published2016
Admission routes2
Has abstractyes

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