Tracking the Gendered Life Courses of Care Leavers in 19th-Century Britain
Bibliographic record
Abstract
The adult outcomes of children raised in care are a matter of much concern in Britain today. Care leavers account for a quarter of the adult prison population, a tenth of the young homeless population, and over two thirds of sex workers (Centre for Social Justice, 2015: 4). This article argues that, by contrast, the first generation of boys and girls passing through the early care system were more likely to have experienced a modest improvement in their life chances. It explores three key questions. First, what mechanisms shaped adult outcomes of care in the past? Second, did these vary by gender? Third, what might life course approaches to these issues gain from engaging both with historical- and gender-inflected analysis? The article draws on our wider analysis of the life courses and life chances of 400 adults who passed through the early youth justice and care systems as children in the northwest of England from the 1860s to the 1920s. These systems were closely interlinked. Within that, the article focuses on the experiences of a subgroup sent to a more care-oriented institution. It compares their collective outcomes with those of the wider group and within-group by gender. It offers a selection of case studies of women’s lives before and after care to highlight the value of, and challenges involved in, undertaking gender analysis in life course research of this kind.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".