Bibliographic record
Abstract
Recent studies reveal an increasing number of instances in which Qallunaat benefited from Inuit knowledge of the lands and waters upon which they had lived for centuries. One of the best recorded examples of Inuit geographical knowledge is found in the story of Eenoolooapik, who led to the European rediscovery of Cumberland Sound 250 years after it was first explored and named by John Davis. Taken as a young man from Baffin Island to Scotland in 1839, Eenoolooapik excited whaling captain William Penny with stories of a large, whale-rich body of water then unknown to European and American whalers. “Eenoo,” as he was popularly called, drew a map of the coastline of eastern Baffin showing a deep bay known by the Inuit as “Tenudiackbeek,” and upon their return the next summer, Penny skeptically followed Eenoolooapik’s directions into a large bay in which the Inuk had spent his childhood. Thus the youngster’s geographical knowledge of his homeland resulted in the opening to whalers of a long-lost body of water in which, in the next decade, shore stations were established that offered seasonal employment to the Inuit and dramatically changed their lives. The story of Eenoolooapik is told in a small book by Alexander M’Donald, A Narrative of Some Passages in the History of Eenoolooapik […] published in Edinburgh in 1841. This is probably the only nineteenth-century full-length biography of an Inuk published during the subject’s lifetime; and because copies of the book are exceedingly rare, the following article provides a synopsis as a means of portraying more fully the geographical contributions of Eenoolooapik.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.052 | 0.019 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".