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Squatter Housing

2020· reference-entry· en· W4212923096 on OpenAlexaff
Alan Smart

Bibliographic record

VenueOxford Research Encyclopedia of Anthropology · 2020
Typereference-entry
Languageen
FieldSocial Sciences
TopicUrban and Rural Development Challenges
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSquatting positionAffordable housingGentrificationValue (mathematics)BusinessPublic housingSecurity of tenureDenialEconomicsEconomic growthLand tenureGeography

Abstract

fetched live from OpenAlex

Abstract Squatting is one of the most important forms of housing for the world’s poor, accommodating perhaps a billion people, with the numbers continuing to grow. Squatters occupy vacant land or buildings without the consent of the owner. Squatting in existing buildings is more common in the Global North, particularly in Europe, and tends to be more political, often explicitly anticapitalist, than squatting on vacant land, which accounts for the vast majority of squatters, particularly in the Global South. Urban squatter housing needs to be seen as valuable housing rather than just as a social problem. Housing generally has exchange value, a price on housing markets, as well as use value, the utility of it for those who live in it. Early research dealt primarily with use value because of the emphasis on self-building and collectively organized invasions of land. Demand for scarce stocks of affordable housing leads to market prices despite governmental denial of the possibility of ownership of illegal dwellings. Squatter housing often meets the needs of poor people more effectively than public housing, and policy initiatives around the world are attempting to enhance the utility of informally built and regulated housing while mitigating the environmental problems that they can cause. Formalizing informal housing is a key but controversial policy. Research has revealed that informal tenure security is considered adequate by residents, resulting in lower than expected demand for squatter titling. Formalization may also lead to gentrification and thus diminishes the abilities of informal housing to provide affordable accommodation.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.836
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.006
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.123
GPT teacher head0.416
Teacher spread0.293 · 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 designNot applicable
Domainnot available
GenreOther

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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