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Record W1985550834 · doi:10.1017/s014754790400002x

Not a “Sack of Potatoes”: Why Labor Historians Need to Take Agriculture Seriously

2004· article· en· W1985550834 on OpenAlexaff
Cindy Hahamovitch, Rick Halpern

Bibliographic record

VenueInternational Labor and Working-Class History · 2004
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAmerican History and Culture
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsProletarianizationPrivilege (computing)Marxist philosophyWorking classSackProletariatSociologyClass (philosophy)AgricultureLabour economicsPolitical scienceHistoryEconomicsLawEngineeringArchaeologyEpistemology

Abstract

fetched live from OpenAlex

Labor and working class historians, whether explicitly Marxist or not, have tended to use models of class and class formation that privilege urban industrial workers, depicting their lived experience as, somehow, hermetically sealed off from the countryside. Despite an awareness of the intertwined and overlapping nature of rural and urban economies, most historians have treated rural workers as either peasants or slaves, and therefore fitting subjects for separate fields of historical inquiry, or as individuals on the cusp of proletarianization, would-be or about-to-be industrial workers. Indeed, even studies focused on recent history have viewed the rural sector as a “backward” one and constructed the industrial setting as “modern.” Thus, even if few labor historians have dismissed agricultural workers with the condescension of Marx's “sack of potatoes,” as a collectivity we have paid them insufficient attention and our field is weaker as a result.

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)0.007
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0060.047
Scholarly communication0.0080.015
Open science0.0010.002
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0030.001

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 head0.010
GPT teacher head0.191
Teacher spread0.181 · 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 designTheoretical or conceptual
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

Citations6
Published2004
Admission routes1
Has abstractyes

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