Development of Cultural and Environmental Awareness Through Sámi Outdoor Life at Sámi/Indigenous Festivals
Bibliographic record
Abstract
The indigenous people Sámi are an ethnic minority living in Finland, Norway, Russia, and Sweden. Throughout history, Sámis have been living close to nature. Working with reindeer husbandry, fishing-farming, hunting, herding, and harvesting for food supplies, has traditionally been an integral part of their lives. Currently, only 2,500 of the ~65,000 Sámis in Norway are operating reindeer husbandry (2019). Most Sámis today work in mainstream jobs, and the fishing-farming culture gradually become more like the mainstream societies where Sámis live. Fieldwork with participant observation and semi-structured interviews carried out at Riddu Riđđu Festivala in the period 2009-2018. In addition, the governing bodies of seven other Sámi festivals have been interviewed. All together 46 in-depth interviews and participant observations conducted, in addition to document analysis of the festivals. The aim was to study how physical and outdoor activities included in the festivals create indigenous people's identities and cultural understanding and how the activities at the festivals might develop climate and environmental awareness. Indigenous festivals and their governing bodies offer many different forms of physical and cultural activities from Sámis and different indigenous peoples to the youth and children taking part. Further, the study shows that important aims for the organizers are to spread the knowledge about Sámis (i.e., local coastal Sámis and regional reindeer/Inland Sámis) and other indigenous peoples, and making environment-friendly festivals. They are trying to educate the children and youth in the cultural practices of their forefathers and foremothers. The manifold of activities offered at the festivals seem to create sustainable ties between persons, which equip the participants with social and cultural capital in addition to networks across festivals organizations internationally. The participants further express that taking part in the festival activities create symbolic capital, due to that they might express their indigeneity at the festivals both for people living in the region and for a greater audience. According to the participants, the festivals have equipped the participants with cultural awareness, as well as the children and youth taught an appreciation of nature so they can enjoy and respect nature and develop climate and environmental awareness.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".