Bibliographic record
Abstract
Canadian politicians, like many of their circumpolar counterparts, brag about their country’s “Arctic identity” or “northern character,” but what do they mean, exactly? Stereotypes abound, from Dudley Do-Right to Northern Exposure, but these southern perspectives fail to capture northern realities. In this passionate, deeply personal account of modern developments in the Canadian North, Tony Penikett corrects confused and outdated notions of a region he became fascinated with as a child and for many years called home. During decades of service as a legislator, mediator, and negotiator, Penikett bore witness to the advent of a new northern consciousness. Out of sight of New Yorkers, and far from the minds of Copenhagen’s citizens, Indigenous and non-Indigenous leaders came together to forge new Arctic realities as they dealt with the challenges of the Cold War, climate change, land rights struggles, and the boom and bust of resource megaprojects. This lively account of their clashes and accommodations not only retraces the footsteps of Penikett’s personal hunt for a northern identity but also tells the story of an Arctic that the world does not yet know.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.015 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".