Developing a Sustainable and Inclusive Northern Knowledge Ecosystem in Canada
Bibliographic record
Abstract
A knowledge ecosystem is a collection of individuals and organizations who are involved in the creation, management and dissemination of knowledge, both in the form of research and lived experience and teaching. As is the case with ecosystems more generally, they thrive on variation and diversity, not only in the types of individuals and organizations involved but also in the roles that they play. For many decades, the northern knowledge ecosystem in Canada was dominated and controlled by Western scholarly approaches and researchers based in academic institutions outside the North. More recently, this research landscape has started to change, largely in response to the efforts of Indigenous peoples and northerners to realize greater self-determination and self-government. Not only have these changes led to the development of research and educational capacity in the North, but they have also changed the way that academic researchers engage in the research process. The keys to maintaining the future sustainability and health of the northern knowledge ecosystem will be encouraging diversity and balance in the research methodologies and approaches used to generate knowledge about the North and ensuring that the needs and priorities of northern and Indigenous peoples are recognized and addressed in the research process.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.027 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".