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Record W4413906628 · doi:10.70121/001c.143862

For Better or For Worse? The Dual Economic Impact of the Industrial Revolution on Women

2025· article· en· W4413906628 on OpenAlexaff

Bibliographic record

VenueScholarly review . · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Labor, and Family Dynamics
Canadian institutionsDoug Bragg Enterprises (Canada)
Fundersnot available
KeywordsDual (grammatical number)Industrial RevolutionEconomicsPolitical scienceArt

Abstract

fetched live from OpenAlex

This paper examines the economic impact of the Industrial Revolution on the lives of working-class and middle-class women. It describes pre-industrial society, where family members laboured together to earn a living in a system known as the family economy. Industrialization disrupted this way of living by moving production from the household into factories. The family wage economy, where individual members laboured for wages to contribute to a common fund, soon replaced the family economy. This paper examines how industrialization changed the lives of women in Britain, France, and the US and finds that the impacts were often divergent. While factory work subjected working-class women to exploitative conditions, low pay, and exclusion from unions, it also provided them with greater independence from the family. In the middle class, the rise of the Cult of Domesticity granted women authority in the domestic sphere and offered new opportunities for female solidarity, but also reinforced restrictive gender norms. This paper argues that the short-term impacts of the Industrial Revolution, both the beneficial and the harmful, catalyzed long-term social change by awakening women’s feminine consciousness.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.006
Scholarly communication0.0030.002
Open science0.0000.001
Research integrity0.0010.001
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 head0.063
GPT teacher head0.358
Teacher spread0.295 · 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 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
Published2025
Admission routes1
Has abstractyes

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