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Record W2558667086 · doi:10.14430/arctic4609

Made in Nunavut: An Experiment in Decentralized Government, by Jack Hicks and Graham White

2016· article· en· W2558667086 on OpenAlexvenueaboutno aff
Alastair Campbell

Bibliographic record

VenueARCTIC · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsWhite (mutation)Government (linguistics)SociologyEnvironmental ethicsPhilosophyBiology

Abstract

fetched live from OpenAlex

Made in Nunavut fills an important gap.Up to now little has been written about the process through which the new territory was formed, in the period from 1993 to 1999, and on the extent to which the hopes and aspirations for that territory have been realized in the years following its establishment.This is the subject matter of Made in Nunavut, with a particular focus on the decentralization of certain functions of the Nunavut government to various communities across the territory.It is a work well suited to students of political science, public administration, and northern studies, primarily at the university level, but for some at a college level as well: it provides an enormous information base.It is written in a non-technical manner, and in this sense is also suited to the general reader.The authors describe this study as a work of two decades.Jack Hicks, we are told, "literally lived the Nunavut decentralization experience" (p.xi).From 1994 to 1996, he was Director of Research for the Nunavut Implementation Commission (NIC), which was the body charged with the administrative design of the first Nunavut government.Later he served as Director of the Evaluation and Statistics Division for the Government of Nunavut, a body that was decentralized to Pangnirtung: a move of which he is very critical (see note 127, p. 361).He thus brings to the study the knowledge of a direct participant.Graham White is a professor of Political Science at the University of Toronto.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.018
GPT teacher head0.305
Teacher spread0.288 · 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 designObservational
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
Published2016
Admission routes2
Has abstractyes

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