Northern Science and Research
Bibliographic record
Abstract
The International Polar Year (IPY) provides an opportunity to reflect on Northern science and research. For all Canadians, science and research should contribute to living a good life. A good life includes successfully making sense of the world within local contexts, sharing this knowledge beyond the immediate community and reconciling it with knowledge held by outsiders. Northern science and research are inherent in Traditional Dene, Inuvialuit and Métis knowledge; and they continue to be reflected in Northern governance, economy, and cultures. Alongside Aboriginal sciences are Western sciences; these are primarily disciplinary in nature and formally structure postsecondary education globally. Postsecondary science and research education is still being introduced to the Northwest Territories (NWT). Over the last forty years the territorial government has developed the capacity for educational services, funding, institutions, and authority through the Department of Education, Culture and Employment. The delivery of Northern-based postsecondary education through Aurora College provides Northerners with the capacity to generate science and research in the North. What place do science and research have in the North? (North in this paper demarcates the socially constructed geopolitical territories north of the 60th parallel that we use cautiously as a structural term for the purposes of our narrative.) What kinds of investments need to be made and will Northerners be prepared to overcome barriers and take advantage of the opportunities?
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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.016 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.047 | 0.012 |
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".