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Record W2085770665 · doi:10.1017/s0032247413000132

Towards a sustainable future for Nunavik

2013· article· en· W2085770665 on OpenAlexaffabout
Thierry Rodon, Stephan Schott

Bibliographic record

VenuePolar Record · 2013
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsCarleton UniversityUniversité Laval
Fundersnot available
KeywordsSubsistence agriculturePaceSocioeconomic statusEnvironmental planningSustainable developmentArcticGeographyPolitical scienceBusinessEconomic growthAgricultureEnvironmental healthPopulationEcologyEconomicsMedicine

Abstract

fetched live from OpenAlex

ABSTRACT The predominantly Inuit Arctic region of Nunavik in the Province of Québec, Canada, currently needs to address major challenges and opportunities. The region needs to develop more employment and wealth creation opportunities without sacrificing the vital land-based subsistence sector that provides food security, sustains cultural identity and provides social and economic stability. A decision about a new direction needs to be taken soon as major mining projects are developing at a rapid pace. In this paper we first assess existing socioeconomic and living conditions data to evaluate the state of social well-being in the region. In addition we report and analyse information from an economic forum in Kuujjuaq, Nunavik in April 2010. The purpose of the forum was to provide an opportunity for regional and local stakeholders to obtain information on specific economic opportunities for Nunavik and to discuss their merit for the communities. Based on our data evaluation and the outcomes at the forum we identify a possible sustainable development feedback envisioning process and discuss possible sustainable development directions for Nunavik.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.189
Threshold uncertainty score0.381

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.002
Scholarly communication0.0040.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.023
GPT teacher head0.352
Teacher spread0.328 · 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 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

Citations15
Published2013
Admission routes2
Has abstractyes

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