Human Security of Inuit and Sámi in the 21st Century: The Canadian and Finnish Cases
Bibliographic record
Abstract
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—Canada’s Arctic and Northern Policy Framework (2019) and Finland’s Strategy for Arctic Policy (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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.057 | 0.016 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".