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Record W4392366865

Making housing a common space : the effort of personal appropriation of space in the context of cohousing

2020· preprint· fr· W4392366865 on OpenAlexaboutno aff
Pierre Servain

Bibliographic record

Venuetheses.fr (ABES) · 2020
Typepreprint
Languagefr
FieldSocial Sciences
TopicCollaborative and Sustainable Housing Initiatives
Canadian institutionsnot available
Fundersnot available
KeywordsAppropriationSpace (punctuation)Context (archaeology)Architectural engineeringSociologyAestheticsEpistemologyEngineeringArtHistoryLinguisticsPhilosophyArchaeology
DOInot available

Abstract

fetched live from OpenAlex

La thèse défendue dans cette recherche est que les manières d’« habiter autrement » dans un habitat participatif constituent un travail de mise en commun de l’habitat, et que ce travail du commun devient lui-même un support qui permet de travailler l’appropriation habitante. Ce travail est pluriel et complexe, car il s’agit de faire des habitats des projets immobiliers, politiques et d’habiter, et un travail sur la propriété. Chacune de ces dimensions sont travaillés selon les principes du commun. Cette mise en commun de l’habitat met à l’épreuve les habitants, aussi bien en termes de groupes que d’individus, elle les engage dans leurs singularités et, in fine, dans leurs capacités à habiter. En retour, cette capacité se trouve renforcée : les manières d’habiter en commun ne renvoient pas seulement à « habiter autrement » mais aussi à « habiter plus ». Au niveau théorique, l’étude se base principalement sur de la sociologie de l’habitat (Segaud, Fijalkow), de la sociologie du commun (Ostrom, Dardot et Laval), la philosophie d’Hannah Arendt et le cadre théorique de la sociologie des épreuves (Breviglieri, Thévenot). Au niveau empirique, l’étude repose sur une enquête qualitative menée entre 2014 et 2018 auprès de16 habitats participatifs de l’Ouest de la France construits sur la période des années 1970 aux années 2010.

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)0.003
metaresearch head score (Gemma)0.004
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: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.025
Scholarly communication0.0060.004
Open science0.0010.008
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.087
GPT teacher head0.348
Teacher spread0.261 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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