Canadian Mountain Assessment: Walking Together to Enhance Understanding of Mountains in Canada
Bibliographic record
Abstract
The Canadian Mountain Assessment provides a first-of-its-kind look at what we know, do not know, and need to know about mountain systems in Canada. The assessment is based on insights from First Nations, Métis, and Inuit knowledges of mountains, as well as findings from an extensive assessment of pertinent academic literature. Its inclusive knowledge co-creation approach brings these multiple forms of evidence together in ways that enhance our collective understanding of mountains in Canada, while also respecting and maintaining the integrity of different knowledge systems. The Canadian Mountain Assessment is a text-based document, but also includes a variety of visual materials as well as access to video recordings of oral knowledges shared by Indigenous individuals from mountain areas in Canada. The assessment is the result of over three years of work, during which time the initiative played an important role in connecting and cultivating relationships between mountain knowledge holders from across Canada. It is the outcome of contributions from more than 80 Indigenous and non-Indigenous individuals and contains six chapters: Introduction Mountain Environments Mountains as Homelands Gifts of the Mountains Mountains Under Pressure Desirable Mountain Futures By way of these chapters, the Canadian Mountain Assessment aims to enhance appreciation for the diversity and significance of mountains in Canada, to clarify challenges and opportunities for mountain systems in the country, and to motivate and inform new research, relationships, and actions that support the realization of desirable mountain futures. More broadly, the Canadian Mountain Assessment provides insights into applied reconciliation efforts in a knowledge assessment context and seeks to inspire similar knowledge co-creation efforts in and beyond Canada.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.030 | 0.004 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".