Bibliographic record
Abstract
Orientation Systems of the North Pacific Rim is an extension of the author’s earlier volume Eskimo Orientation Systems (also published in the series Monographs on Greenland | Meddelelser om Grønland, Man & Society, 1988). This time it covers all the contiguous languages – and cultures – across the northern Pacific rim from Vancouver Island in Canada to Hokkaido in northern Japan, plus the adjacent Arctic coasts of Alaska and Chukotka. These form a testing ground for recent theories concerning the nature and classification of orientation systems and their shared ‘frames of reference’, in particular the many varieties of ‘landmark’ systems typifying the Arctic and sub-Arctic. Despite the wide variety of languages spoken here (all of them endangered), there is much in common as regards their overlapping geographical settings and the ways in which terms for orientation within the microcosm (the house) and within the macrocosm (the surrounding environment) mesh throughout the region. This is illustrated with numerous maps and diagrams, from both coastal and inland sites. Attention is paid to ambiguities and anomalies within the systems revealed by the data, as these may be clues to pre-historic movements of the populations concerned – from a riverine setting to the coast, from the coast to inland, or more complex successive displacements. Cultural factors over and beyond environmental determinism are discussed within this broad context.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".