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Record W1852441247 · doi:10.1111/anti.12110

Subprime Housing Goes South: Constructing Securitized Mortgages for the Poor in Mexico

2014· article· en· W1852441247 on OpenAlexaff
Susanne Soederberg

Bibliographic record

VenueAntipode · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicUrban and Rural Development Challenges
Canadian institutionsQueen's University
Fundersnot available
KeywordsSecuritizationLatin AmericansCapital (architecture)SubsidyPower (physics)MaterialismState (computer science)EconomicsFinancial systemMarket economyPolitical scienceGeographyLaw

Abstract

fetched live from OpenAlex

Abstract Mexico represents the largest market for residential mortgage‐backed securitization (RMBS) in Latin America. Despite its significance to questions of development, there has been no critical analysis on the social implications and power dimensions of RMBS with regard to low‐income housing in Mexico. This essay fills this gap by demystifying the technical and thus apolitical nature of RMBS as well as by explaining how and why state‐sponsored securitization schemes subsidize financial and construction interests in the name of expanding home ownership for the poor. In so doing, the analysis employs a historical materialist approach that, first, places RMBS within the contradictory nature of capital accumulation processes in Mexico and relations of class‐based power therein, and second, views RMBS as an integral feature of housing policy that is inextricably linked to the nerve centre of capital accumulation, namely: the credit system.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.036
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.003
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.036
GPT teacher head0.296
Teacher spread0.259 · 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

Citations71
Published2014
Admission routes1
Has abstractyes

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