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Record W4413957715 · doi:10.1080/03086534.2025.2551256

Displacement and Resilience: Scottish Highland Communities in Nova Scotia, 1810–1850

2025· article· en· W4413957715 on OpenAlexafffundabout
S. Karly Kehoe

Bibliographic record

VenueThe Journal of Imperial & Commonwealth History · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsSaint Mary's University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsNova scotiaResilience (materials science)Displacement (psychology)Nova (rocket)GeographyArchaeologyEngineeringPsychologyPhysicsAeronauticsPsychoanalysis

Abstract

fetched live from OpenAlex

This article is a historical intervention in understanding the roots of socio-economic exclusion by interrogating the links between people’s displacement from three of Scotland’s Western Isles – Barra, Eigg, and South Uist – and their (re)settlement in the ‘wilderness lands’ of Cape Breton Island or Unama’ki, as it is known by the Mi’kmaq. Exploring patterns of economic deprivation and social dislocation illuminates some of British imperialism’s more obscure threads such as the complex legacies of migration and colonisation. Scottish Highland out-migration and settlement occurred within two distinct but entangled contexts – a colonised people at home and a colonising people abroad. For those who engaged willingly and early, the material benefits could be significant and included access to land, fish, furs, and timber. In places like the Maritime colonies of northeastern British North America, it also offered cultural respite and the prospect of religious tolerance. For those who had not engaged as willingly or willingly at all, however, the benefits and long-term prospects were much more limited. Recognising the complexities and diversity inherent within the migrant and settler experiences illuminates some of the hidden factors that shaped the prospects of generations of people in this Atlantic region.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.275
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.282
Teacher spread0.265 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2025
Admission routes3
Has abstractyes

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