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Record W2603863028 · doi:10.7910/dvn/vqqhci

Happiness and House Prices in Canada: 2009-2013

2016· dataset· en· W2603863028 on OpenAlexaffabout
Hussaun A. Syed

Bibliographic record

VenueHarvard Dataverse · 2016
Typedataset
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsHappinessDemographic economicsEconomicsControl (management)Logistic regressionScale (ratio)PsychologyGovernment (linguistics)SocioeconomicsSocial psychologyGeographyStatisticsMathematics

Abstract

fetched live from OpenAlex

The purpose of this study was to understand the relationship between happiness and housing prices in Canada. The happiness data were obtained from the General Social Survey between 2009 and 2013, asking respondents to report overall happiness level by using scale ranging between 1 to 10 points. House Price Indexes at the provincial level were constructed to cover the same period. The relationship between average house price change and average happiness was estimated using Ordinary Least Square and Logistic Regression techniques. Individual's characteristics were used as control variables. The study found that average happiness level is positively and significantly related to the change in housing prices for one group and not for another - for homeowners but not for renters. In addition, individuals with better health are much happier than individuals with poor health. Similarly, individuals with higher income are happier than individuals with less income. The implication of this study is that the government should design attractive policies to encourage homeownerships.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.024
Threshold uncertainty score0.174

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.015
Science and technology studies0.0020.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.017
GPT teacher head0.182
Teacher spread0.165 · 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 designNot applicable
Domainnot available
GenreDataset

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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