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A Tale of Two Crises in Peru: Livelihoods and Social Reproduction During the 1980s and the COVID-19 Pandemic

2022· article· en· W4280559089 on OpenAlexafffundvenue
Susan Vincent, Patrick Clark, Aparicio Chanca Flores

Bibliographic record

VenueAnthropologica · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsYork UniversitySt. Francis Xavier University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsFinancializationLivelihoodReciprocity (cultural anthropology)PeasantNeoliberalism (international relations)PandemicDevelopment economicsSocial reproductionEconomic growthPolitical sciencePolitical economySociologyEconomicsGeographyAgricultureEconomyCoronavirus disease 2019 (COVID-19)Social scienceMarket economySocial capital

Abstract

fetched live from OpenAlex

The COVID-19 pandemic has wreaked havoc on the livelihoods ofpeople around the world. Structural economic constraints are highlightedat such moments of crisis, while those most affected have recourse to theirrepertoire of managing strategies. This case study of people from Allpachico,a Peruvian peasant community, compares their responses to the current crisiswith their responses to one in the 1980s, showcasing similarities in strategies(especially reciprocity and the sale or exchange of necessary reproductive tasksand products) and differences in the form they take. In the 1980s, women’s workand kin reciprocity helped people access use-values. By 2020, neoliberalismhad transformed the national economy and Allpachiqueño migrantsoverwhelmingly had precarious informal and contract work. Reciprocity andreproductive tasks are still central to livelihood, but now tend to be monetizedrather than involving use-values. As that earlier crisis shattered both secureemployment and peasant farming to lay the basis for neoliberalism, so now itappears that the COVID-19 pandemic, through the monetization of governmentsupport and reciprocity alike, is accelerating financialization in the form offinancial services and debt.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.184
Threshold uncertainty score0.888

Codex and Gemma teacher scores by category

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

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 head0.330
Teacher spread0.243 · 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 designObservational
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
Published2022
Admission routes3
Has abstractyes

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