‘How many Eskimo words for ice?’ Collecting Inuit sea ice terminologies in the International Polar Year 2007–2008
Bibliographic record
Abstract
Inuit knowledge of the sea ice environment has been praised by generations of early explorers, arctic travellers, natural scientists, anthropologists, and popular writers. Surprisingly little has been done to systematically document and analyze the richness of the Inuit sea ice nomenclatures until quite recently. This article reviews the history of Inuit (Eskimo) sea ice terminology collection, including efforts undertaken in 2005–2009 for the International Polar Year (IPY) 2007–2008. Altogether, a database of 35 indigenous ice nomenclatures from the Bering Sea to East Greenland has been created, displaying the richness of over 1,500 terms for sea ice in all Inuit/Eskimo languages and most regional dialects, as well as in other indigenous northern languages (Chukchi, Dena’ina Athabascan, and Sámi). Processing these vocabularies, analyzing the origins and historical geography of the Inuit sea ice nomenclatures, and returning the data to participating communities as educational, heritage, and language materials may become one of the lasting contributions of the IPY 2007–2008 program.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".