Bibliographic record
Abstract
The Northern Review has always aspired to be a broad and inclusive academic journal, focusing on northern topics, contributions from northern residents, enthusiastically multidisciplinary, and open to new and provocative voices. Issue Number 49 continues in the well-established tradition.This issue focuses on two important themes: the development of the often neglected Provincial North in Canada, and the evolution of place-based sustainability research. The latter theme reminds us of the contemporary challenge of moving beyond the rhetoric of empowering northerm communities, to doing the hard work of transforming the passion for locally controlled sustainability into practical and effective action. The focus on the Provincial North in Canada, which has been an important analytical priority for the Northern Review for some time, draws attention to the divisions within the region. The Provincial North has ten times the population of the Territorial North, a more robust resource economy, and a wealthier population. It also has some of the poorest communities in Canada, serious infrastructure deficits, almost no political autonomy, and a great deal of control exercised by provincial governments. The Provincial North also attracts little attention from the Government of Canada. Number 49 also includes a fascinating set of general articles. The Northern Review celebrates the diversity of research and analysis that is an integral part of the “new North.” The papers in this section celebrate a variety of voices and perspectives, encouraging young scholars, non-academics, practitioners, and others to contribute their ideas. Equally, the journal continues to celebrate different ways of knowing and sharing ideas. One of the great strengths of the Northern Review is the breadth and range of the topics and ideas it presents. We invite readers to explore this issue with an open mind and a sense of intellectual curiosity. The North—territorial, provincial, and circumpolar—is an important and fascinating place. Enjoy your exploration and your encounter with the diverse and fast-growing intellectual traditions here.
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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.275 | 0.205 |
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".