Bibliographic record
Abstract
Occasionally, several articles published in the same year converge upon a quite specific theme.Sometimes, the convergence may be attributed to newly available data, for example, from population censuses, or to a significant anniversary, for example, the centenary of the First World War.Other times, it may arise from an ambitious programme of research having come to fruition.Yet, especially for the heavily studied period from 1850 to 1945, there are occasions when the clustering of articles around a closely circumscribed theme happens, seemingly, by chance.These serendipitous convergences afford readers of economic and social history a more comprehensive treatment of topics than would have been expected.One of the purposes of this review (and one of the joys of the reviewer) is to bring these convergences to the attention of economic and social historians.In 2021, no less than five articles converged, serendipitously, on the topic of British emigration to Australasia.These articles spanned six authors and three journals, viz.Australian Economic History Review, Historical Research, and Social History of Medicine.Relying on shipping records, Ward estimates that, during the period from 1852 to 1915, approximately 20 per cent of British emigrants to the colony of Victoria eventually returned to Britain.The returning migrants, who were mostly women, were strongly influenced by social networks.Ward's article includes both quantitative analysis and biographical vignettes of returning migrants.One of these migrants was Annie Martlew, who emigrated from Liverpool to Melbourne in 1884, only to return two decades later as a widow with five young children, motivated by the existence of a stronger support network in Britain.Social networks are a potential explanation for a paradox identified by Hatton in his study of emigration from the United Kingdom to the Anglosphere during the late nineteenth century.Surprisingly, he finds that emigrants to Australia and New Zealand were, on average, more skilled than emigrants to Canada and the United States, despite wages exhibiting a higher skill premium in North America.Hatton speculates that the lower skill content of emigrants from the United Kingdom-importantly, including Ireland-to the United States (vis-à-vis migrants to Australasia) might be due to the presence of greater networks of previous migrants there.Leaving aside the skill content of migrants, the sheer number of migrants to Australasia was positively influenced by
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.045 | 0.040 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.141 | 0.101 |
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".