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Record W7043844438

Towards a Law of Inclusive Planning: A Response To “Fair Housing for a Non-Sexist City”

2021· article· en· W7043844438 on OpenAlexaff

Bibliographic record

VenueeYLS (Yale Law School) · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicFeminism, Gender, and Sexuality Studies
Canadian institutionsColumbia College
Fundersnot available
KeywordsPublic housingRace (biology)Housing discriminationInequalityFair Housing ActFocus (optics)Urban planningRight to the city
DOInot available

Abstract

fetched live from OpenAlex

Noah Kazis’s important article, Fair Housing for a Non-sexist City, shows how law shapes the contours of neighborhoods and embeds forms of inequality, and how fair housing law can provide a remedy. Kazis surfaces two dimensions of housing that generate inequality and that are sometimes invisible. Kazis highlights the role of planning and design rules – the seemingly identity-neutral zoning, code enforcement, and land-use decisions that act as a form of law. Kazis also reveals how gendered norms underlie those rules and policies. These aspects of Kazis’s project link to commentary on the often invisible, gendered norms that shape the design of ordinary objects, public space, data, and automated algorithms.\nAs to housing specifically, Kazis’s emphasis on gender is noteworthy; most examinations of exclusion in housing and land use concern race and class. Kazis takes up the invitation of Professor Dolores Hayden, a prominent urban historian, to imagine how we might redesign urban spaces and rethink the connection between the city and suburb. Kazis’s focus on “sex” means not just women as a broad category, but women who own businesses, participate in the wage economy, and need childcare zoned in their neighborhoods, as well as men who are low-income and need single-room occupancy (SRO) and other housing arrangements to make housing affordable.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.748
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.058
GPT teacher head0.370
Teacher spread0.312 · 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.

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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueeYLS (Yale Law School)Same topicFeminism, Gender, and Sexuality StudiesFrench-language works237,207