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Record W2026612919 · doi:10.1080/02763893.2011.595618

Housing Costs in an Oil and Gas Boom Town: Issues for Low-Income Senior Women Living Alone

2011· article· en· W2026612919 on OpenAlexaffabout
Laura Ryser, Greg Halseth

Bibliographic record

VenueJournal of Housing for the Elderly · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Aging, and Tourism Studies
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsRetrenchmentCost of livingPovertyEconomic growthBusinessHousehold incomeOil boomStandard of livingSocioeconomicsEconomicsGeographyPolitical science

Abstract

fetched live from OpenAlex

In resource-based towns that have historically been dominated by young workers and their families, seniors’ housing issues have received little attention by community leaders and senior policymakers. However, since the 1980s there has been a growing trend of older women living alone in Canadian rural and small town places. Although research on rural poverty focuses on small towns in decline, booming resource economies can also produce challenges for low-income senior women living alone due to higher housing costs and the retrenchment of health care and service supports. Because housing costs can consume a significant proportion of household income, low-income senior women living alone may not have the financial resources to cover expenses in a competitive housing market. Using a household survey, we explored this different dimension of the Canadian rural landscape by looking at housing costs for low-income senior women living alone in the booming oil and gas town of Fort St. John, British Columbia, Canada. The authors findings indicate that low-income senior women living alone are incurring higher housing costs compared with other senior groups.

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 score0.821
Threshold uncertainty score0.361

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.001
Scholarly communication0.0020.001
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 head0.029
GPT teacher head0.301
Teacher spread0.273 · 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

Citations28
Published2011
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Housing for the ElderlySame topicMigration, Aging, and Tourism StudiesFrench-language works237,207