The Victorian Sublunary Heaven: Emigration and Tom Arnold’s “Antipodistic” Romance
Bibliographic record
Abstract
D uring the nineteenth century, millions of people departed the United Kingdom for permanent settlement in North America, South Africa, Canada, Australia, and New Zealand. Calling this mass transfer of peoples the “Settler Revolution,” historian James Belich records that while the greatest number left for North America, a surprisingly large group traveled further to the colonies of Australia and New Zealand during two booms of emigration lasting from 1828 to 1841 and from 1847 to 1867 ( Replenishing the Earth 261, 548). Charlotte Erickson likewise observes that by 1841, prospective emigrants were presented with a great variety of colonies (168), yet many chose Australasia through the new system of “assisted emigration” (173, 196). Victorian public interest in emigration and settlement in new lands is evident from the countless literary texts featuring it as a dream destination or place to conveniently dispose of characters. Fortunes were made there and brought back to the mother country in fiction and in fact; often they were lost before they could be enjoyed. And in the late 1840s, middle-class writers frequently presented emigration as a possible solution to working-class suffering. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.016 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".