Bibliographic record
Abstract
‘Empire’ and ‘globalisation’ are currently two of the most prominent and widely debated discourses in the humanities and social sciences. This book explores the historical relationship between them. We take as our starting-point one of the great global movements of population – the largely voluntary emigration of men, women and children from Europe to the New World between the mid nineteenth century and the First World War. While migration may be ‘as old as humanity itself’, it was during these years that the world witnessed an unprecedented exodus of 50 million or so Europeans. Britain led the way, supplying approximately 13.5 million migrants, or a quarter of the total. Aided by improvements in transport and communications, arguably no less dramatic in their ability to transform life than those witnessed over the last half-century, the majority of these British people settled across Australia, New Zealand, South Africa, Canada and the United States. The consequences of this population outflow were profound. On the one hand, emigration was a force for global economic growth – integrating labour, commodity and capital markets to an extent never previously seen. Yet, on the other, this business of white settlement – for that is was it was, or at least became – led to the widespread dispossession and oppression of indigenous peoples, as well as to a racialisation of the social order, the polarising effects of which were felt powerfully at the time and still resonate today.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.354 | 0.193 |
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".