The expansion of basic education during ‘deskilling’ technological change in England and Wales, <i>c</i>. 1780–1830
Bibliographic record
Abstract
Abstract The first country to industrialize – England – ostensibly did so without expanding investment in the basic education of its workforce. The empirical evidence underpinning this argument for England rests largely on signature rates at marriage. These are not a perfect indication of educational achievement, particularly as many children never learned to write. More problematically, I argue signatures are likely to have systematically underestimated human capital in industrial districts. In place of signature data, I propose age heaping, a measure widely understood as a proxy for numeracy but shown here to be closely related to both reading and writing abilities. In contrast to signatures, this measure suggests that ‘deskilling’ industrialization induced human capital accumulation. I argue that this occurred not because human capital was directly productive, but rather because schools provided a valuable signal. Sunday school attendance signalled low leisure‐preference among child workers and were popularly attended in industrial districts. Further, such schools taught children to read but not write, which they considered inappropriate for the Sabbath, accounting for the discrepancy between these two measures of human capital.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".