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Record W4413226262 · doi:10.1017/s0018246x25100964

Age, Gender, and Agency in Juvenile Migration from England to Canada, 1850–1900

2025· article· en· W4413226262 on OpenAlexaboutno aff
Gillian Lamb

Bibliographic record

VenueThe Historical Journal · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsEmigrationScholarshipHistoriographyGender studiesAgency (philosophy)BlameEmpirePolitical scienceSociologySocial sciencePsychologySocial psychologyLaw

Abstract

fetched live from OpenAlex

Abstract This article makes two important contributions. Firstly, it provides valuable insights into the motivations of working-class migrants in the second half of the nineteenth century, adding a new dimension to a scholarship focused on studies of forced migration or middle-class empire building. Its analysis of a rich body of published and unpublished letters from former institutionalized children reveals the primacy of financial gain in the migration decision and shows that working-class Britons saw the world beyond the British Isles as a space of opportunity, where they could leverage their mobility in pursuit of profit. Secondly, by arguing that juvenile emigrants need to be viewed as a heterogeneous body where age and gender made a difference in terms of experience, the article provides an important new perspective on institutional migration that has implications for wider literatures on childhood and youth. The average age of the boys studied for this article was sixteen and the research shows that they were active participants in the emigration process, shaping their own futures through their diverse decisions. Recognizing this significantly undermines the modern discourses of blame and victimhood that dominate the historiography and encourages us to re-evaluate our approach to nineteenth-century juvenile migration.

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)0.003
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: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.350

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0120.004
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.017
GPT teacher head0.236
Teacher spread0.218 · 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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