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Record W2549752499 · doi:10.3138/cjh.49.3.451

<i>Child Workers and Industrial Health in Britain, 1780–1850</i>, by Peter Kirby

2014· article· en· W2549752499 on OpenAlexvenueno aff
Samantha Williams

Bibliographic record

VenueJournal of History · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHistorical Economic and Social Studies
Canadian institutionsnot available
Fundersnot available
KeywordsChild labourPopulationOvercrowdingSanitationIndustrialisationPovertyFactory (object-oriented programming)Work (physics)SociologyEconomic growthEconomicsPolitical scienceMedicineLawDemographyEngineering

Abstract

fetched live from OpenAlex

Child Workers and Industrial Health in Britain, 1780-1850, by Peter Kirby. People, Markets, Goods: Economies and Societies in History, Vol. 2. Woodbridge, Boydell Press, 2013. xii, 212 pp. 17.99 [pounds sterling] UK (paper). In this study Kirby seeks to dispel the myth that child workers in industrial occupations necessarily suffered worse health due to the nature of their work than did other child workers. He argues that this idea stemmed largely from the misleading and exaggerated claims of refonners in the 1830s factory debates and that it has not previously been the subject of rigorous research. As Kirby demonstrates, factory work was generally less intensive than other forms of child labour, such as agriculture, mining, and even domestic manufacture. This careful work revises a long-held stereotype. The book builds well on Kirby's other research on child labour. The author begins his study with an exploration of the wider environment of manufacturing, which was one characterized by massive population growth and rapid urbanization, with its associated problems of overcrowding, poor sanitation, poverty, and increased illness in and death of infants and children. Rising child dependency (with almost forty percent of the population aged under fifteen) was mirrored by increases in the supply of child labour and poorer children were put into work at early ages, particularly those who had lost a parent, a finding shared by other recent historians of child labour. The author then assesses the specific hazards of the factory system for child workers in terms of the risk of deformities, the effect of raw materials, industrial injuries, and ill-treatment. He then examines the relative impact of the wider manufacturing environment and the workplace setting upon the heights and strength of child workers, with Kirby giving greater weight to environment over occupational health. However, as Kirby expertly highlights, there are significant methodological problems in isolating occupational health from the wider epidemiological and environmental causes of sickness in this period, a relative dark age of information on morbidity and mortality, which is unfortunate given that this was a major period of occupational and epidemiological transitions. Kirby draws upon modern studies of occupational medicine and the health of child workers in developing economies in order to help him illuminate the difficult contemporary evidence. Stories of poor child health became integral to the Ten Hours Movement and white slavery. Most of the medical practitioners giving evidence to parliamentary select committees had little or no experience of the factory work they were commenting upon. The author concludes that the medical profession actually played no role in improving child industrial ill-health and may even have diverted attention away from it. …

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.085
Threshold uncertainty score0.168

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0020.004
Scholarly communication0.0030.004
Open science0.0010.003
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0080.004

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 head0.031
GPT teacher head0.201
Teacher spread0.170 · 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 designNot applicable
Domainnot available
GenreReview

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
Published2014
Admission routes1
Has abstractyes

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