MétaCan
Menu
Back to cohort
Record W4386285893 · doi:10.3138/jcs-2022-0009

Racial Capital, Public Debt, and the Appropriation of Epekwitk, 1853–1873

2023· article· en· W4386285893 on OpenAlexaffvenue
Angela Tozer

Bibliographic record

VenueJournal of Canadian Studies · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicAustralian History and Society
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsColonialismAppropriationIndigenousDebtGovernment (linguistics)CapitalismPublic financeEconomicsPolitical economyEconomyPolitical scienceLawFinancePolitics

Abstract

fetched live from OpenAlex

This article argues that public debt financing facilitated the appropriation of the territories of Indigenous nations in the British settler colonies and does so through a detailed examination of Prince Edward Island’s public debt. The island’s government used public debt financing as a technique to direct capital into the colony, but to receive loans, the colonial government first needed credit. Settler-colonial credit derived from colonial governments’ claiming the territories of Indigenous nations as government assets. This history highlights the deeply racial characteristics embedded in the expansion of global public debt financing that characterized finance capitalism beginning in the 1820s. In this way, the unique history of the island and its “land question” can be placed into the broader global context of debt markets and processes of racial capital. Specifically, the 1853 Land Purchase Act used public borrowing to purchase lands from British landowners so that the island’s government could hold the land title. British landowners had their ownership rights secured despite the eighteenth-century Peace and Friendship Treaties that guaranteed the Mi’kmaq nation rights to their territory, which included Epekwitk, what the British named Prince Edward Island.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.321
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.089
GPT teacher head0.322
Teacher spread0.233 · 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 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

Citations2
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Canadian StudiesSame topicAustralian History and SocietyFrench-language works237,207