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Record W2748293833 · doi:10.1080/10361146.2017.1364342

Class, capital, and identity in Australian society

2017· article· en· W2748293833 on OpenAlexaboutno aff
Jill Sheppard, Nicholas Biddle

Bibliographic record

VenueAustralian Journal of Political Science · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational and Educational Inequality Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSocial classQuarter (Canadian coin)Demographic economicsUnemploymentMiddle classSocial capitalLatent class modelIdentity (music)Cultural capitalPolitical scienceGeographyEconomic growthSociologySocial scienceEconomics

Abstract

fetched live from OpenAlex

Despite a comparatively ‘flat’ social structure and lack of obvious class-based cleavages, Australian society is stratified by objective, multidimensional measures of social class. Using data from a July 2015 survey of a random sample of Australian citizens, latent class analysis identifies six class types in Australian society, based on the distributions of cultural, social, and economic capital among respondents. The resulting classes are categorised as ‘precariat’, ‘ageing workers’, ‘new workers’, ‘mobile middle’, ‘emerging affluent’, and ‘established affluent’. The precariat is characterised by high numbers of retired pensioners, the ageing worker class the highest mean age, and the new worker class by its low rate of unemployment. The established middle class accounts for one quarter of the adult population, while the emergent affluent class has the youngest mean age, and the established affluent is the most advantaged. We also show Australians are acutely aware of their class identity.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.134

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.003
Scholarly communication0.0030.001
Open science0.0000.004
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.116
GPT teacher head0.460
Teacher spread0.344 · 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

Citations62
Published2017
Admission routes1
Has abstractyes

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