On the edge: a consideration of the adaptive capacity of Indigenous Peoples in coastal zones from the Arctic to the Tropics
Bibliographic record
Abstract
Abstract Indigenous peoples occupy many of the world's coastal zones, and have used and managed coastal and marine resources for millennia. As a result they have accumulated extensive knowledge about these environments that supported their ability to colonize new territories in the past and informs their on-going adaptive responses to environmental, climatic and socio-cultural changes. This paper examines the adaptive capacity of Indigenous peoples over time and across a range of coastal settings. It begins with a discussion of some reasons for the relative neglect, until recently, of cultural adaptations in coastal settings. The experiences of a selection of Indigenous groups in each of three coastal zones – Arctic, Temperate and Tropical – are then explored in relation to various adaptive mechanisms they have employed traditionally as well as in contemporary contexts. Comparison across these experiences indicates that Indigenous peoples regard the coast as an integral component of a land–sea continuum that provides enhanced food security, important cultural and spiritual attachments, and valuable opportunities for social interaction and learning. Coastal science and policy initiatives stand to benefit from greater consideration of the adaptive strategies of Indigenous coastal peoples.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".