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Record W2801245165 · doi:10.1111/tran.12229

Race, debt and empire: Racialising the Newfoundland financial crisis of 1933

2018· article· en· W2801245165 on OpenAlexfundaboutno aff
Declan Cullen

Bibliographic record

VenueTransactions of the Institute of British Geographers · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHistorical Economic and Social Studies
Canadian institutionsnot available
FundersMemorial University of NewfoundlandNational Science Foundation
KeywordsFinancial crisisEmpireCapitalismDebtColonialismDominionPolitical economySovereigntyEconomyEconomicsEconomic historyFinancePolitical scienceLawPoliticsKeynesian economics

Abstract

fetched live from OpenAlex

The recent global financial crisis and related sovereign debt crises in Ireland, Greece, Iceland, Puerto Rico and beyond have highlighted the pressing task of understanding how such crises reshape the spaces we live in. Geographers, most notably David Harvey, have traced the historical roots of the current crisis to the transformation of global financial capitalism since the 1970s. There has, however, been less work on understanding the nature and management of financial crises embedded in different historical geographies. This paper seeks to contribute to that task by investigating Newfoundland's sovereign debt crisis during the Great Depression and its management by the British Empire. Newfoundland, then an independent British Dominion, uniquely relinquished self‐government in return for financial aid. Managing the crisis required difficult ideological work. I argue that to reinforce an imperial geography underpinned by racial distinctions, and to preclude the possibility of default, Newfoundland was scripted as a racially degenerate place in need of metropolitan intervention. The financial crisis produced new racialised geographies that had significant effects on Newfoundland's future.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.294
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.016
GPT teacher head0.212
Teacher spread0.196 · 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 designObservational
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

Citations7
Published2018
Admission routes2
Has abstractyes

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