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Record W2484722975 · doi:10.1093/0198297688.003.0008

Citizenship and the Challenge of Aboriginal Self‐Government: Is Deep Diversity Desirable?

2000· book-chapter· en· W2484722975 on OpenAlexaboutno aff
Joseph H. Carens

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicReligious Freedom and Discrimination
Canadian institutionsnot available
Fundersnot available
KeywordsCitizenshipGovernment (linguistics)PoliticsCharterPolitical scienceEconomic JusticeDiversity (politics)Cultural diversityCultural assimilationEnvironmental ethicsSociologyPolitical economyPublic administrationLaw

Abstract

fetched live from OpenAlex

Abstract Explores the possibilities of reconciling the demands of aboriginal peoples in Canada for forms of self‐government that will reflect and protect their distinct cultural traditions with the idea of a shared Canadian citizenship based on equality and political unity. It outlines the long history of the use of Canadian citizenship as a tool of coercive assimilation of First Nations people in Canada and argues that this history justifies considerable wariness on their part toward any project of civic integration. It also considers the question of whether the cultural differences between aboriginal people and other Canadians would warrant some limitations on the application of the Charter of Rights and Freedoms (Canada's Bill of Rights) to aboriginal people. Finally, the chapter argues that a unitary model of citizenship is bound to fail to achieve the civic integration of aboriginal people. It contends that a version of differentiated citizenship that makes dialogue over justice and cultural difference central is the best hope for achieving civic integration, though it is an approach that carries its own risks.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.276
Threshold uncertainty score0.548

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0060.015
Scholarly communication0.0050.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.252
Teacher spread0.231 · 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 source (direct Gemma or distilled Codex), 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

Citations21
Published2000
Admission routes1
Has abstractyes

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