Eyewitness to a Whale Hunt: Theory, Event, and Apology in the Inuit North
Bibliographic record
Abstract
The paper revisits a whale hunt that took place in the vicinity of the hamlet of Pangnirtung, Nunavut, in 1998. An eyewitness to the hunt, I wrote a Master’s thesis, “The Bowhead Whale Hunt at Kekerten, Nunavut Territory (July 1998),” giving a chronicle of its duration and preparation. The hunt was undertaken by the Inuit as a way of dealing with a haunted piece of their past: the catastrophic aftermath of the presence of European whalers in the region in the 19th and early 20th centuries, which deeply challenged both the physical and cultural survival of the Inuit of the Eastern Arctic. The hunt was carried out at a significant site of memory/ lieu de mémoire: a former whaling station in proximity to the community. An important aspect of the Pangnirtung hunt was the assertion of Inuit collective identity in the claim of sovereignty over the management of bowhead whale stock in the months preceding the creation of Nunavut, in April 1999. Yet departing from conventional interpretations that would understand the Inuit as the recipients of a “gift” in a cycle of forgiveness and restitution (apology-as-discourse), I argue that the event itself was not the result, but the very substance of apology. Bringing the past forward into the present, the Inuit of Pangnirtung introduced an event as a critical requirement in undertaking the work of apology.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.031 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".