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Record W628778489

A ‘Greater Britain’: the Creation of an Imperial Landscape, 1880-1914

2013· dissertation· en· W628778489 on OpenAlexaboutno aff
Robyn Elizabeth Cooper

Bibliographic record

VenueLeicester Research Archive (University of Leicester) · 2013
Typedissertation
Languageen
FieldSocial Sciences
TopicAustralian History and Society
Canadian institutionsnot available
Fundersnot available
KeywordsEmpireFrontierBritish EmpireModernityRepresentation (politics)EmigrationPeriod (music)HistoryPopulationGeographyEthnologyGenealogyAncient historySociologyArchaeologyArtAestheticsPolitical scienceLawDemography
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.289
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.048
GPT teacher head0.334
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

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