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Record W2424431200 · doi:10.1057/9781137293688_10

Crisis, Social Class, and the ‘Fixing’ of Capitalism in Mexico

2014· book-chapter· en· W2424431200 on OpenAlexaff
Hepzibah Muñoz Martínez

Bibliographic record

VenuePalgrave Macmillan UK eBooks · 2014
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsAusterityUnemploymentPovertyFinancial crisisDebtRecessionInvestment (military)EconomicsCapitalismHousehold debtEconomyDevelopment economicsEconomic policyPolitical scienceEconomic growthFinance

Abstract

fetched live from OpenAlex

In 2007 the American economy experienced a severe crisis which spread through credit and financial markets, leading to declining rates of investment, lower consumption and growing unemployment in the United States. In an interview at the end of 2008, Agustin Carstens, former Minister of Finance (2006–9) in Mexico and head of the Mexican Central Bank between 2010 and 2016, stated that economic stagnation in the United States would have a limited effect on the Mexican economy. When the interviewer noted that Mexico usually catches ‘pneumonia’ when the United States has an economic ‘cold’, the minister responded that this time the Mexican economy would only ‘catch the sniffles’ (Notimex 2008). He believed that Mexico’s sound policies of fiscal austerity, public debt management and reserve accumulation would protect its economy from external shocks (Gil Diaz 2009: 29–31). However, this did not occur, and Mexico’s GDP declined 6 per cent in 2009 (INEGI 2011a). According to Mexico’s National Council on the Evaluation of Social Development Policy (CONEVAL), the number of people living in poverty increased by 3.2 million between 2008 and 2010 (CONEVAL 2011). The transmission mechanisms linking the American financial crisis to the Mexican economy were diminishing exports to, and workers’ remittances from, the United States, Mexico’s main trading and investment partner. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.117
Threshold uncertainty score0.232

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.005
Scholarly communication0.0040.002
Open science0.0000.003
Research integrity0.0010.002
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 head0.020
GPT teacher head0.208
Teacher spread0.188 · 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

Citations3
Published2014
Admission routes1
Has abstractyes

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