Bibliographic record
Abstract
Northern Canada's distinctive landscapes, its complex social relations and the contested place of the North in contemporary political, military, scientific and economic affairs have fueled recent scholarly discussion. At the same time, both the media and the wider public have shown increasing interest in the region. This timely volume extends our understanding of the environmental history of northern Canada - clarifying both its practice and promise, and providing critical perspectives on current public debates. Ice Blink provides opportunities to consider critical issues in other disciplines and geographic contexts. Contributors also examine whether distinctive approaches to environmental history are required when studying the Canadian North, and consider a range of broader questions. What, if anything, sets the study of environmental history in particular regions apart from its study elsewhere? Do environmental historians require regionally-specific research practices? How can the study of environmental history take into consideration the relations between Indigenous peoples, the environment, and the state? How can the history of regions be placed most effectively within transnational and circumpolar contexts? How relevant are historical approaches to contemporary environmental issues? Scholars from universities in Canada, the United States and Britain contribute to this examination of the relevance of historical study for contemporary arctic and sub-arctic issues, especially environmental challenges, security and sovereignty, indigenous politics and the place of science in northern affairs. By asking such questions, the volume offers lessons about the general practice of environmental history and engages an international body of scholarship that addresses the value of regional and interdisciplinary approaches. Crucially, however, it makes a distinctive contribution to the field of Canadian environmental history by identifying new areas of research and exploring how international scholarly developments might play out in the Canadian context. With contributions by: Tina Adcock, Stephen Bocking, Emilie Cameron, Hans M. Carlson, Marionne Cronin, Matthew Farish, Arn Keeling, P. Whitney Lackenbauer, Tina Loo, Paul Nadasdy, Jonathan Peyton, Liza Piper, John Sandlos, Andrew Stuhl
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.672 | 0.371 |
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".