MétaCan
Menu
Back to cohort
Record W59061324 · doi:10.18584/iipj.2010.1.1.4

Indigeneity-Grounded Analysis (IGA) as Policy(-Making) Lens: New Zealand Models, Canadian Realities

2010· article· en· W59061324 on OpenAlexaffvenueabout
Augie Fleras, Roger Maaka

Bibliographic record

VenueInternational Indigenous Policy Journal · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsAotearoaIndigenousNexus (standard)PoliticsSociologyGovernment (linguistics)Argument (complex analysis)Gender studiesEnvironmental ethicsPolitical scienceLawEcology

Abstract

fetched live from OpenAlex

Engaging politically with the principles of indigeneity is neither an option nor a cop out. The emergence of Indigenous peoples as prime-time players on the world’s political stage attests to the timeliness and relevance of indigeneity in advancing a new postcolonial contract for living together differently. Insofar as the principles of indigeneity are inextricably linked with challenge, resistance, and transformation, this paper argues that reference to indigeneity as policy(- making) paradigm is both necessary and overdue. To put this argument to the test, the politics of Maori indigeneity in Aotearoa New Zealand are analyzed and assessed in constructing an indigeneity agenda model. The political implications of an indigeneity-policy nexus are then applied to the realities of Canada’s Indigenous/Aboriginal peoples. The paper contends that, just as the Government is committed to a gender based analysis (GBA) for improving policy outcomes along gender lines, so too should the principles of indigeneity (or aboriginality) secure an indigeneity grounded analysis (IGA) framework for minimizing systemic policy bias while maximizing Indigenous peoples inputs. The paper concludes by theorizing those provisional first principles that inform an IGA framework as a policy (-making) lens.

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, 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: none
Teacher disagreement score0.860
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.002
Science and technology studies0.0130.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.359
Teacher spread0.332 · 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

Citations23
Published2010
Admission routes3
Has abstractyes

Explore more

Same venueInternational Indigenous Policy JournalSame topicIndigenous Health, Education, and RightsFrench-language works237,207