A ‘Greater Britain’: the Creation of an Imperial Landscape, 1880-1914
Bibliographic record
Abstract
This thesis examines the representation of the settler societies of the British Empire during the late nineteenth and early twentieth centuries. Australia, New Zealand, Canada and South Africa were represented as a distinct part of the Empire, united by the idea that these parts of the Empire were ‘more British’ than the rest, and, had a shared heritage and culture and a predominant British settler population. It was represented as a landscape of opportunity built on layers of representations in the sources of the period from advertisements and panoramas to travel accounts and emigration literature. \nThe settler societies were represented as a ‘Greater Britain’ or ‘Better Britains’, an imagining of the settler societies based on what the British wanted for themselves rather than as a true representation of four parts of the Empire. The notion of ‘Better Britains’ delves into British ideas of their past, present and future. If they were ‘better’, what were they improving on? What qualities and aspects of society were included and excluded? It was an idealised image but also flexible, a malleable landscape where the British could live out desires. Opportunity was found in the land, resources and climate, but also within the modernity of the cities and ideas of social advancement and of the freedom of the frontier.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".