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Record W7098019837

The implications of labour market change for first time buyers

2001· article· en· W7098019837 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsnot available
Fundersnot available
KeywordsCasualMetropolitan areaWelfareInvestment (military)Sample (material)Quarter (Canadian coin)Job insecurityUnemploymentFull-time
DOInot available

Abstract

fetched live from OpenAlex

This paper reports on research into the implications of labour market change for a sample of 500 first time home buyers in South Australia (SA). Increased job insecurity is presumed to be impacting on home ownership aspirations and sustainabilty across all income groups but is largely untested. While Australia has one of the highest casual employment rates among developed countries, SA stands out within the nation as being the most precarious labour market. SA experienced a net loss of 20,000 full time jobs in the decade 1990 to 2000 with part time and casual employment the main job growth area. Australia’s welfare and housing polices have been predicated for fifty years on the perceived merits of home ownership. Welfare benefits both during employment and on retirement have been based on household investment being extended over time through home ownership. Thus any significant change in purchaser behaviour particularly in the cohort who would traditionally have entered home ownership as early as possible, merits review. The main research instrument was a postal survey of first time homebuyers who made their purchase during the period 1st January 1999 to 31st December 2000. The study area included the Adelaide metropolitan area and the rural townships of Mount Gambier, Murray Bridge and Port Lincoln. The survey aimed to determine first time buyer profiles including the nature of their employment, their expectations and attitudes to job security, and if and how, this had influenced the timing, location, borrowing arrangements, or nature of their home purchase. The paper reports on the findings and policy implications of the research.

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)0.002
metaresearch head score (Gemma)0.006
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 score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.042
GPT teacher head0.231
Teacher spread0.190 · 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
Published2001
Admission routes1
Has abstractyes

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