Waifs, strays, and foundlings: illegitimacy, gender, and youth migration from Britain, 1870–1930
Bibliographic record
Abstract
Assisted child migration from Britain is a deeply controversial subject. “Rescue” societies sent over 100,000 children to the empire in less than a century in a movement riddled with class and racial assumptions. Migration to the Dominions supposedly solved labor problems in both Britain and the Dominions, and authorities hoped that “white” migrants to Africa would delay demands for independence (Langford, 2013). The effects of migration on the children, however, varied widely. Many left families behind; some societies allowed migrants to correspond with their kin, but others did all they could to sever family connections. Similarly, those who received migrants in Canada and Australia exhibited an array of behaviors; some exploited the children ruthlessly, while others adopted them as family. The issue of identity and power was also fraught, since the children's identities both supported and undermined hierarchies of empire. As “white” migrants, British children were part of the hegemonic racial structure, but they were also paupers and minors. In short, the complexities are seemingly endless (Bean and Melville, 1989; Parr, 1994; Sherington and Jeffery, 1998; Neff, 2000; Parker, 2010; Swain and Hillel, 2010; Boucher, 2014). Unsurprisingly, historians have struggled to encompass the many ambiguities of youth migration. Early works (Bagnall, 1980; Bean and Melville, 1989; Humphreys, 1994; Parr, 1994; Gill, 1997) highlighted the harm done to the children, but later historians noted that reactions spread across a large spectrum. For instance, certain organizations were more diligent about after-care than others (Langfield, 2004, 2013). Individuals also sometimes experienced their migration as positive—gaining control over their own fates, achieving upward mobility, or finding personal happiness. Migration was part of a structure of dominance over the poor, yet it offered the opportunity to reconstitute identity, as part of the ruling race for example, or as independent workers (Howard and Leonard, 1999). Furthermore, recent works have shown gender and race distinctions. Boys, after all, were the most common migrants, since girls’ ability to have children made it dangerous to send them abroad (Chaudhuri, 1998; Diamond, 1999). Boys’ migration interacted both with the hope to build proper masculinity in male youths and a desire to support “whiteness” in the empire (Parker, 2010; Swain and Hillel, 2010; Boucher, 2014).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".