Bibliographic record
Abstract
assistance. Over the last one hundred and fifty years, the quantity of formal education provided for most children has risen dramatically. At any time, school enrollment rates have varied substantially across countries, and within countries they have differed by region and family background. We all know that, generally, richer societies have more children at school than poorer societies, and at the level of the individual family, wealthier families are and were more likely to send their children to school, and to send them to school for a longer period, than poor families (e.g. Crafts, 1985). Raising average education levels has long been seen as a means to promote economic development, and reducing income / class / gender barriers to attending school considered as important ways to reduce the incidence of poverty. In this paper, we examine how much schooling was available, and the kinds of children who went to school, in urban Canada at the beginning of the twentieth century. Around 1900, the United States, Australia, and the United Kingdom were, in terms of per capita income, the richest countries in the world (Table 1). Their educational policies were dramatically different, especially with respect to attendance of adolescents. 1 At the beginning of the century, roughly 60-65 % of (white) American 14 year olds were at school, about 40 % of Australian 14-year olds, but only about 10 % of those in the UK. 2 Not only were all of these countries high-income, they were all English-speaking, and mainly Protestant. Thus one might expect a broadly similar approach to education to have been chosen, when in fact patterns of 1 Children in the UK and Australia started school younger than those in the US or Canada.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.858 | 0.784 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".