How is the Land linked to the brain?
Bibliographic record
Abstract
Strategies to improve mental health must include Indigenous knowledge and ways of living and expression, starting with acknowledging the fundamental importance of connection between the Land and the brain. In this chapter we present and discuss stories about places and land. These stories are fundamental for all people, as they ensure that memories and knowledge are kept alive and thus guide us. Stories teach us how to understand ourselves and how to interact in our relationships with things known and unknown, in a landscape populated by our past and our present. We use Fjellheim’s concept of Sámi cultural landscape to show how ways of living, knowing and being are integrated into ongoing cultural connection to the Land. Fjellheim’s mapping of the Sámi cultural landscape, which analyses the route of South Sámi reindeer herders at Røros, provides a theoretical framework for understanding the relationship between cognition, feelings, attitudes, values, social relations and the Land. We continue with an auto-ethnographic process and set out to investigate which stories about place and Land matter for us, and why. These stories carry memories of the past and experiences we, or people we know, have had. We then present a story of how Land is related to mental health through detailing the impact of wind farms on Sami youth. The colonial past and present harms the connection to cultural landscape, and wants to deny Indigenous relations to Land.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".