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Record W2990835223 · doi:10.4324/9781351142045-9

Landscape and settler nationalism in the “white dominions”

2018· book-chapter· en· W2990835223 on OpenAlexaboutno aff
Lize van Robbroeck, Damian Skinner

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicAmerican Environmental and Regional History
Canadian institutionsnot available
Fundersnot available
KeywordsNationalismWhite (mutation)GeographyHistoryPolitical scienceLawBiologyPolitics

Abstract

fetched live from OpenAlex

This chapter focuses on the role played by art, and in particular landscape, in the establishment of emerging settler national identities in the dominions during the 1930s, with particular emphasis on Canada, South Africa, and New Zealand. It shows that a common settler speaking position is articulated via visual culture and surrounding discourses, to give voice to emerging national identities at a time of intensive settler nation-building, and that both landscape and modernism served as apparatuses for this task in all the British dominions. Identifying such a settler speaking position that cuts across the settler domains suggests that national distinctiveness, which is the mainstay of nation-building, is a myth. The chapter also focuses on the myth of settler national exceptionalism and identifies a common settler speaking position articulated through the genre of landscape and the appropriation of a moderate modernist vocabulary. The settler nationalism promoted by Contemporary Canadian Painting followed the terms established by the Group of Seven.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.067
Threshold uncertainty score0.134

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0060.007
Scholarly communication0.0040.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.008
GPT teacher head0.187
Teacher spread0.179 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2018
Admission routes1
Has abstractyes

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