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Record W4310187261 · doi:10.46328/ijres.2974

Sámi Early Childhood Education and Sustainability in the Arctic

2022· article· en· W4310187261 on OpenAlexaboutno aff
Marikaisa Laiti, Kaarina Määttä, Mirja Köngäs

Bibliographic record

VenueInternational Journal of Research in Education and Science · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsArcticIndigenousSettlement (finance)Early childhood educationMetisSustainabilityTraditional knowledgeEducation for sustainable developmentEnvironmental educationSociologySustainable developmentGeographyPolitical scienceEconomic growthPedagogyEcologyBusiness

Abstract

fetched live from OpenAlex

The Sámi are indigenous people living in Finland, Norway, Sweden, and Russia. There are about 10,500 Sámi in Finland. The traditional settlement area of the Sámi is located in the Arctic. Endangered Inari, Skolt, and Northern Sámi languages are spoken in Finland, and efforts are made to implement the traditions, principles, and values of indigenous culture. The traditional settlement area of the indigenous Sámi people is in the Arctic. The Sámi culture and languages are in a vulnerable position due to their present climate change. Early childhood education (ECE) is of particular value to contribute to the preservation and strengthening of indigenous culture and, consequently, to sustainable development in the Arctic. The purpose of this article is to describe Arctic sustainable Sámi early childhood education based on the perceptions and experiences of Sámi early childhood educators in Finland. The research shows that cultural sustainability was approached by using Sámi language in activities, supporting children’s Sámi identity, using materials and items important in culture, and having a tight connection with Sámi community.

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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.003
Scholarly communication0.0030.001
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.379
Teacher spread0.364 · 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 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

Citations5
Published2022
Admission routes1
Has abstractyes

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