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Record W4206983546 · doi:10.1007/s10982-021-09414-w

What a Home Does

2022· article· en· W4206983546 on OpenAlexafffund
David Jenkins, Kimberley Brownlee

Bibliographic record

VenueLaw and Philosophy · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsUniversity of British Columbia
FundersAll Souls College, University of OxfordCanada Excellence Research Chairs, Government of CanadaLeverhulme Trust
KeywordsPolitical philosophySociologyProperty (philosophy)Core (optical fiber)Key (lock)Philosophy of lawSocial workPublic relationsLaw and economicsPoliticsLawPolitical scienceEpistemologyComputer securityComputer science

Abstract

fetched live from OpenAlex

Abstract Analytic philosophy has largely neglected the topic of homelessness. The few notable exceptions, including work by Jeremy Waldron and Christopher Essert, focus on our interests in shelter, housing, and property rights, but ignore the key social functions that a home performs as a place in which we are welcomed, accepted, and respected. This paper identifies a ladder of home-related concepts which begins with the minimal notion of temporary shelter , then moves to persistent shelter and housing , and finally to the rich notion of a home which focuses on meeting our social needs including, specifically, our needs to belong and to have meaningful control over our social environment. This concept-ladder enables us to distinguish the shelterless from the sheltered; the unhoused from the housed; and the unhomed from the homed. It also enables us to decouple the concept of a home from property rights, which reveals potential complications in people’s living arrangements. For instance, a person could be sheltered but unhoused, housed but homeless, or, indeed, unhoused but homed. We show that we should reserve the concept of home to capture the rich idea of a place of belonging in which our core social needs are met.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.564
Threshold uncertainty score0.616

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)0.0010.000

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 head0.025
GPT teacher head0.205
Teacher spread0.181 · 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 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

Citations13
Published2022
Admission routes2
Has abstractyes

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