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Record W4293177597 · doi:10.4337/9781788119023.00013

Indigenous peoples and electoral law

2022· book-chapter· en· W4293177597 on OpenAlexaboutno aff
Andrew Geddis

Bibliographic record

VenueEdward Elgar Publishing eBooks · 2022
Typebook-chapter
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousAotearoaExpansionismPolitical scienceDemocracyState (computer science)PoliticsPolitical economyRepresentation (politics)LawSociology

Abstract

fetched live from OpenAlex

Indigenous populations pose a particular challenge to the electoral laws of settler-states; that is, those present-day liberal representative democracies founded upon a territorially expansionist or colonial past. For the claim of recognised Indigenous peoples is not simply that their interests should be granted equal recognition within a society's collective decision-making processes, but rather that they should be permitted to define and advance their interests for and by themselves. Furthermore, the role of settler-states in imposing and overseeing any electoral mechanisms used by recognised Indigenous peoples to identify and pursue their own collective interests must be determined. These matters go to the heart of the nature of the settler-state and its relationship to those recognised Indigenous peoples who exist within, but also separate from, that political arrangement. Using the concrete examples of Aotearoa New Zealand, Nunavut and the Sámi Sámediggi, this chapter explores strategies used to reconcile liberal-democratic notions of elected representation with recognition of Indigenous peoples' right to self-determination.

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)0.002
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 score0.049
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.008
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.028
GPT teacher head0.270
Teacher spread0.242 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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