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Record W2554329511 · doi:10.1007/s13280-016-0834-1

What’s counted as a reindeer herder? Gender and the adaptive capacity of Sami reindeer herding communities in Sweden

2016· article· en· W2554329511 on OpenAlexafffund
Astri Buchanan, Maureen G. Reed, Gun Lidestav

Bibliographic record

VenueAMBIO · 2016
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsUniversity of Saskatchewan
FundersNational Eye InstituteSocial Sciences and Humanities Research Council of CanadaSveriges Lantbruksuniversitet
KeywordsHerdingLivelihoodAdaptive capacityPsychological resilienceConceptualizationAnimal husbandrySocial capitalGeographySocioeconomicsPolitical scienceEconomic growthSociologyAgricultureEcologyEconomicsSocial scienceClimate changePsychologyBiologySocial psychology

Abstract

fetched live from OpenAlex

Researchers of adaptive capacity and sustainable livelihoods have frequently used social, cultural, human, economic and institutional capitals to better understand how rural and resource-dependent communities address environmental, social and economic stresses. Yet few studies have considered how men and women contribute differently to these capitals to support community resilience overall. Our research sought to understand the differential contributions of Sami men and women to the adaptive capacity of reindeer husbandry and reindeer herding communities in northern Sweden. Our focus revealed a gendered division of labour in reindeer herding as an economic enterprise as well as gendered contributions to a broader conceptualization of reindeer husbandry as a family and community-based practice, and as a livelihood and cultural tradition. Based on our results, we recommend that community resilience be enhanced by generating more opportunities for men to achieve higher levels of human and economic capital (particularly outside of herding activities) and encouraging women to contribute more directly to institutional capital by participating in the formation and implementation of legislation, policies and plans.

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)0.002
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
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 head0.119
GPT teacher head0.367
Teacher spread0.248 · 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

Citations31
Published2016
Admission routes2
Has abstractyes

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