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Record W2943703805 · doi:10.4324/9780203222997-10

What Women’s Spaces? Women in Australian, British, Canadian and US Suburbs

2003· book-chapter· en· W2943703805 on OpenAlexaboutno aff
Veronica Strong‐Boag, Isabel Dyck, Kim England, Louise Johnson

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldArts and Humanities
TopicAmerican Political and Social Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsGender studiesGeographyHistoryGenealogySociology

Abstract

fetched live from OpenAlex

Women, families, and suburbia: for more than one hundred years the three have been intertwined in Australia, Britain, Canada, and the United States. Suburbs exist in that critical fluid region between city centres and rural spaces. While individual suburbs may change remarkably over time and range widely in their specifics, their quintessential representation identifies them as low-density, familycentred residential spaces, sometimes revealingly characterized as ‘bedroom’ or ‘dormitory’ communities. Although differentiated in many ways across the four countries, such imagined suburbs lie at the heart of many discourses about modernity, forecasting either national promise or nightmare. Women and their work, or, more broadly, gender relations haunt the majority of these accounts. However, sustained deconstruction of the ‘taken-for-granted’ association between women and ‘the family’ within residential suburbs had to await the arrival of feminist scholars in the late-twentieth century.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.110
Threshold uncertainty score0.222

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0170.010
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.017
GPT teacher head0.204
Teacher spread0.187 · 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 designQualitative
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

Citations7
Published2003
Admission routes1
Has abstractyes

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