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Record W7111654373

Land to Levy: Taxation as a Tool of Settler Colonialism

2025· article· en· W7111654373 on OpenAlexaboutno aff

Bibliographic record

VenueOpen Access Journals at BC (Boston College) · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousColonialismSovereigntyLegitimacyGovernment (linguistics)RevenueState (computer science)Possession (linguistics)
DOInot available

Abstract

fetched live from OpenAlex

In both the United States and Canada, the taxation of Indigenous people is often viewed as an innocent tool for financing government operations. In the settler colonial context, however, taxation is not strictly a tool for financing the government – it is a powerful instrument for racial domination and forced assimilation of Indigenous people. This paper examines how colonial legal systems have utilized taxation not purely as a method of revenue collection but as a means to both secure white settler possession and to force assimilation on Indigenous people in order to delegitimize Indigenous sovereignty and enforce the Eurocentric definition of economic productivity and citizenship. Additionally, these taxations cause disproportionate financial effects on Indigenous communities and limit their economic opportunities. Drawing on the works of scholars Brenna Bhandar and Cheryl Harris, this analysis will reveal how taxation serves not solely as a method of revenue collection, but as an apparatus for upholding settler colonialism. From historical policies that explicitly categorized Indigenous people as “civilized” or “uncivilized” based on their tax status to contemporary rhetoric that frames Indigenous communities as economic burdens, taxation’s purpose is to maintain racial and economic inequities and reinforce the legitimacy of the settler colonial project.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.760
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0060.000
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.053
GPT teacher head0.449
Teacher spread0.396 · 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 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 routes1
Has abstractyes

Explore more

Same venueOpen Access Journals at BC (Boston College)Same topicIndigenous Health, Education, and RightsFrench-language works237,207