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Record W3146962305 · doi:10.5840/envirophil2021325106

How Do Houses Make the Political Possible?

2021· article· en· W3146962305 on OpenAlexvenueno aff
Joshua Mousie, Gabriel Eisen, Mahaa Mahmood

Bibliographic record

VenueEnvironmental Philosophy · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical and Economic history of UK and US
Canadian institutionsnot available
Fundersnot available
KeywordsPoliticsDisplacement (psychology)SociologyEnvironmental ethicsEveryday lifePolitical sciencePolitical economyLawPsychologyPhilosophy

Abstract

fetched live from OpenAlex

We develop the concept “political residency” in this essay to highlight both the foundational role of built environments in our political life as well as how access to, and displacement from, built environments is therefore a central feature of political harms and goods. The example of housing and housing displacement is instructive for developing our concept because it is central to most people’s everyday life, yet residential security and stability—having control with other inhabitants over shared, built spaces—is often missing from peoples’ lives, especially those who are most socially and politically vulnerable.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.726
Threshold uncertainty score1.000

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.234
Teacher spread0.210 · 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 designTheoretical or conceptual
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

Citations1
Published2021
Admission routes1
Has abstractyes

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