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Record W3009582995 · doi:10.1111/joac.12359

Labour, nature, and exploitation: Social metabolism and inequality in a farming community in mid‐19th century Catalonia

2020· article· en· W3009582995 on OpenAlexfundno aff
Inés Marco, Roc Padró, Enric Tello

Bibliographic record

VenueJournal of Agrarian Change · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicAgriculture Sustainability and Environmental Impact
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaMinisterio de Economía y Competitividad
KeywordsWage labourEconomicsAgricultureInequalityWageLabour economicsDivision of labourLivestockHoarding (animal behavior)CommodityChild labourConsumption (sociology)SociologyEcologyMarket economyBiology

Abstract

fetched live from OpenAlex

Abstract Exploiting the labour of other people has historically been one of the main strategies to tackle the biophysical tension that always exists between the satisfaction of human needs and the labour required to fulfil them. Based on the insights of ecological, feminist, and Marxist economics, we disentangle the exploitation of the labour of women and labouring poor through a novel methodology that integrates energy, material, time, and cash balances. We apply it to the sociometabolic flows between household units endowed with different land and livestock resources in a traditional rural community in Catalonia (Spain) in the mid‐19th century. The results show that land and livestock hoarding led to a process of accumulation through dispossession that increased the exploitative relationships through the labour market, which in turn relied on the patriarchal division of labour between men and women at home. Our estimates of energy labour surplus reveal that male wages represented 88% of the equivalent consumption basket that would have been obtained by carrying out the same amount of labour on land of one's own. However, in the case of female wages, the percentage was 54%. This shows that wage labour incorporated a significant amount of unpaid domestic family labour.

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)0.000
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 score0.217
Threshold uncertainty score0.396

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.036
GPT teacher head0.264
Teacher spread0.228 · 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

Citations13
Published2020
Admission routes1
Has abstractyes

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