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Record W4388825738 · doi:10.17645/pag.7254

Human Security of Inuit and Sámi in the 21st Century: The Canadian and Finnish Cases

2023· article· en· W4388825738 on OpenAlexaboutno aff
Céline Rodrigues

Bibliographic record

VenuePolitics and Governance · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsArcticGeopoliticsIndigenousHuman rightsDemocracyPolitical scienceHuman securityPoliticsGeographyDiversity (politics)The arcticPublic administrationEconomic growthLawEcologyEconomicsOceanography

Abstract

fetched live from OpenAlex

In a changing territorial and geopolitical moment of the Arctic region, are the Indigenous Peoples Organizations heard at the regional level and are the Arctic states working to keep them safe and secure? To safeguard the human security of Arctic Indigenous peoples, Arctic states (and their governments) have to understand the needs and changes that are affecting their way of life as well as to be able to cooperate between them. In a comparative study of Canada’s and Finland’s Arctic policies—<em>Canada’s Arctic and Northern Policy Framework</em> (2019) and <em>Finland’s Strategy for Arctic Policy</em> (2021)—it is possible to identify the applicability of the human security approach, which is influenced by the truth and reconciliation process between Canada and Inuit and Finland and Sámi. This process is a main factor in having their human rights respected and their human security safeguarded, considering that the relation between the countries of the North and the South of the Arctic countries is a discovery of their diversity (linguistical and cultural) in the 21st century. In my perspective, and for a participative democracy to be applied as mentioned by the green political theory (following the views of scholars like Barry, Eckersley, and Goodin), states and governments need to be open and recognise the gaps identified by those communities and transnational organisations.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.861
Threshold uncertainty score0.596

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 studies0.0010.001
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 head0.027
GPT teacher head0.306
Teacher spread0.279 · 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 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

Citations2
Published2023
Admission routes1
Has abstractyes

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