Bibliographic record
Abstract
ABSTRACT This article is concerned with the knowledge of sea ice as developed and transmitted by the Inuit of Igloolik (Nunavut, Canada). The information on which this article is based was obtained from travelling, observation, and interviewing carried out from October 2000 to May 2001 in Igloolik, as well as several existing interviews from the Igloolik Oral History Project database. Inuit knowledge of sea ice reveals a deep understanding of the complex relationships between ice, currents, the Moon, and the winds, as well as a holistic approach to knowledge where classification based on a western scientific approach becomes difficult, if not counter-productive. Through detailed knowledge of ice topography, sea ice becomes a familiar territory for the Inuit of Igloolik, and, through the understanding of the ‘codes’ of the moving ice, its changing nature becomes predictable. This article does not pretend to give a full account of a system of knowledge the understanding of which requires a lifetime of practice and observation. However, it describes some of its elements and offer some insights regarding this complex aspect of Inuit Qaujimajatuqangit (Inuit knowledge, also known as IQ).
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| 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".