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Record W3178284888 · doi:10.1080/22423982.2021.1935132

“If the reindeer die, everything dies”: The mental health of a Sámi community exposed to a mining project in Swedish Sápmi

2021· article· en· W3178284888 on OpenAlexfundno aff
Hanna Blåhed, Miguel San Sebastiån

Bibliographic record

VenueInternational Journal of Circumpolar Health · 2021
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsMental healthThematic analysisBattleLivelihoodPower (physics)PsychologyQualitative researchSociologyHistoryArchaeologyPsychiatrySocial science

Abstract

fetched live from OpenAlex

In 2006, a British mining company started the process of extracting ore from Gállok/Kallak, in Swedish Sápmi. These grounds are used all year round for reindeer herding by the Sámi community Jåhkågasska tjiellde. While environmental impact assessments should be conducted by law in any development project in Sweden, the health component included is usually vague. The aim of this study was to understand the experiences and perceptions of the Sámi community regarding the current and potential health effects of the proposed mine.A qualitative study, including six in-depth interviews with members of the community, was conducted in 2020. Interviews were analysed using thematic analysis. Five themes were identified and organised in current and future impacts. Current impacts included "It's like David's battle against Goliath", "It's a slow process that takes a lot of power and energy", "It's a defense … like, to protect oneself"; with future impacts including: "If the reindeer die, everything dies", "You would feel that you do not possess any power, [you would feel] overridden, pushed away, not liked".The fear of losing current and future generations' livelihoods appeared to be the main mediators of the current and potential worsened mental health experienced by the 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 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)0.003
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.395
Threshold uncertainty score0.345

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.304
Teacher spread0.273 · 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 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

Citations10
Published2021
Admission routes1
Has abstractyes

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