Labour, nature, and exploitation: Social metabolism and inequality in a farming community in mid‐19th century Catalonia
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".