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Record W3119668222 · doi:10.26522/ssj.v14i2.2286

Against National Sovereignty: The Postcolonial New World Order and the Containment of Decolonization

2021· article· en· W3119668222 on OpenAlexvenueno aff
Nandita Sharma

Bibliographic record

VenueStudies in Social Justice · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsnot available
Fundersnot available
KeywordsSovereigntyDecolonizationNationalismRhetoricColonialismPoliticsPolitical economyPolitical scienceSociologyOrder (exchange)Law

Abstract

fetched live from OpenAlex

In this paper, I examine the growing reliance on discourses of autochthony in nationalisms throughout the world. Native-ness (or indigeneity) is increasingly being made a key criterion for claiming national sovereignty over territory, as well as the more amorphous – but no less consequential – claim to national membership. By examining the crucial colonial genealogy of autochthonous discursive practices, I argue that claims to autochthony are metaphysical and, as such, deeply depoliticizing of the exclusions they produce. Drawing upon historical studies showing how imperial-states deployed autocthonous discourses to divide those they categorized as Natives and Migrants from one another in an effort to maintain their imperial rule, I show the continuities of such practices in the Postcolonial New World Order of nation-states. Despite their rhetoric, I argue that contemporary, nationalist discourses of autochthonies have not – and cannot – succeed in realizing decolonization, precisely because of their reliance on modes of political, economic, and social exclusion based on the separation of people categorized as either Native-Nationals or as Migrants. The material force of ideas of Native-Nationalism(s), because they are premised on territorial sovereignty and not on the end of practices of expropriation and exploitation across the planet, are part of the worldwide relations of ruling and not threats to it.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.674
Threshold uncertainty score0.950

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
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 head0.040
GPT teacher head0.379
Teacher spread0.339 · 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 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

Citations6
Published2021
Admission routes1
Has abstractyes

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