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Record W2032914535 · doi:10.1068/a39320

Accommodating Open Plan: Children, Clutter, and Containment in Suburban Houses in Sydney, Australia

2008· article· en· W2032914535 on OpenAlexaboutno aff
Robyn Dowling

Bibliographic record

VenueEnvironment and Planning A Economy and Space · 2008
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsnot available
Fundersnot available
KeywordsOpen planPlan (archaeology)Materiality (auditing)SociologyNarrativeExpansiveContext (archaeology)ArchitectureGender studiesAestheticsHistoryCivil engineeringEngineeringArchaeologyArt

Abstract

fetched live from OpenAlex

Open-plan living areas are one of the defining features of contemporary suburban architecture in spatially expansive nations like Australia, the United States, and Canada. In these contexts, the European and modernist meanings of ‘open plan’ are joined by relations between parents and children and identities associated with motherhood and homemaking. Using a feminist and material-culture analysis of the practices of living open plan in this distinctive historical and geographical context, this paper offers a different interpretation of the social significance of the open-planned domestic interior. Drawing on research conducted with residents of new, open-planned houses on the outskirts of Sydney and, in particular, mothers' narratives of the materialities of their home and the place of children and open plan within it, I show how open plan is held together, as a material and imaginative space, by a balancing of aesthetic considerations and the materiality and anxieties produced by children. The processes of living open plan entailed accommodating family ideals and practices to suit the house and/or altering the house to suit the family: banishing children from open-plan areas to maintain their simplicity; embracing children's presence; placing furniture to enclose it; knocking out walls to open it.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.035
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.050
GPT teacher head0.228
Teacher spread0.178 · 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 teacher head, 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

Citations75
Published2008
Admission routes1
Has abstractyes

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