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Record W2259906005

Natural Resource Exploration and Extraction in Northern Canada: Intersections with Community Cohesion and Social Welfare

2014· article· en· W2259906005 on OpenAlexaffvenueabout
Prescott C. Ensign, Audrey R. Giles, Jacquelyn Oncescu

Bibliographic record

VenueJournal of rural and community development · 2014
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsUniversity of ManitobaUniversity of OttawaWilfrid Laurier University
Fundersnot available
KeywordsCommunity cohesionHuman settlementNatural resourceFrontierCohesion (chemistry)WelfarePoliticsPolitical scienceEconomic growthDevelopment economicsEconomyGeographyEconomics
DOInot available

Abstract

fetched live from OpenAlex

This paper examines the role that the search for and removal of non-renewable fossil fuels plays in northern, often Aboriginal, communities in Canada. Such settlements at the social, political, and geographic periphery or frontier of Canada are often characterized by transient populations and social welfare challenges. While the economic boom brought about by oil and gas development is undeniable, it is unevenly spread. Further, communities that would otherwise be facing sizable challenges now must address even greater and more urgent struggles. These rural and remote settlements have drawn strength from their social cohesion, but presently, the strain is heightened. Insiders may be at odds with outsiders; one generation may be divided against the generation before and after it. Environmental concerns and traditional culture may be displaced by competing interests. In this paper we provide an overview of the existing and proposed extraction of non-renewable natural resources in several parts of northern Canada and examine their economic impact, but also their social impact. In particular, we focus on their ramifications in terms of community cohesion in general and on Aboriginal communities more specifically. Keywords: Canada, north, Aboriginal, community cohesion, social welfare, economic development

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.771
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0080.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.031
GPT teacher head0.297
Teacher spread0.266 · 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 designQualitative
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

Citations14
Published2014
Admission routes3
Has abstractyes

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