Bibliographic record
Abstract
The most obvious way to improve one’s lot in Victorian rural England was to leave it. The fact that most larger towns and villages grew in population while smaller villages usually went into decline after mid century in spite of a high birth rate and a declining death rate, indicates that people did leave in considerable numbers. Most of those leaving Bedfordshire villages left for local towns and for London. There was some attempt to encourage families to migrate to the industrial north, although some contemporaries complained that it was difficult to get men to go even as far as Northampton or Birmingham. Those who did move found that wages were on the whole better in the north, but some were caught out by unforeseen troubles, such as the cotton famine during the U.S. civil war. Emigration overseas was frequently in the foreground of attention. The poor law authorities encouraged farm labouring families to emigrate to the United States and, later in the century, there was an increasing official emphasis on Canada and on the Australian colonies and New Zealand. Australia, however, had to overcome a deep seated prejudice because of its past association with transportation. Emigration was one issue where both the civil authorities, such as the poor law unions, and the farm workers’ own trade unions saw eye to eye, and which both promoted as a means of reducing the chronic over population of rural districts. Migration The incumbent of Cranfield encouraged unemployed labourers to move to Mellor in Derbyshire. James Kay, M.P., reported on their changed conditions, 1836. Mellor, in Derbyshire. Mr. Clayton’s mill. Mr. Clayton expressed great satisfaction with the docility and good conduct of his new work-people. He would not have one Irish-man at Mellor on any account. His mill is in a secluded situation, and he has taken great care to select good hands. He thinks he could not keep Irish workmen in subordination, and considers them decidedly inferior workmen. His new hands from Bedfordshire are very gentle in their manners, and have acquired a knowledge of their employment with great rapidity. Mr. Clayton is quite satisfied with their whole demeanour. The Mill is situated in a most romantic valley, and the cottages of the work-people are scattered over the neighbouring hills, in very healthy situations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".