Bibliographic record
Abstract
In Mountain Voices , alpinists, activists, artists, and mountain researchers share the ways Canadian mountains have impacted their lives. Each contributor brings a unique and fascinating perspective to the mountain landscape with short essays accompanied by a pair of photographs from the remarkable archive of the Mountain Legacy Project, illustrating the history, geography, and lasting inspiration of the mountains. Mountain Voices draws on the vast bank of historic and repeat photographs produced by the Mountain Legacy Project, the world's largest systematic and comprehensive collection of mountain photographs, spanning more than a century. From fragile glass plate negatives to modern, high-resolution photography, these images document a mountain landscape during times of drastic change. Mountain Voices features a diverse array of voices, including Indigenous activists, employees of Canada's national parks, interdisciplinary scientists dedicated to mountains, alpine adventurers, and historians captivated by tales of mountain pasts. Mountain Voices brings the landscape to life through the passion and devotion of those who love it deeply. With contributions by : Leanne Allison, Renellta Arluk, Rick Arthur, Catrin Brown, Bruce Cockburn, Alison Criscitiello, Joanna Croston, Jill Delaney, Winston Delorne, Julie Fortin, Paulette M. Fox, Will Gadd, Ben Gadd, Eric Higgs, David Hik, Aerin Jacob, David P. Jones, Gùdia (Mary Jane) Jonson, Michelle Koppes, Roger Laurilla, Nikita Lopoukhine, Bruce Mayer, Bernadette McDonald, Ella Molnar-Piché, Pat Morrow, Peter Murphy, Liza Piper, Graeme Pole, Martin F. Price, Sara Renner, Jeanine Rhemtulla, Chris Rhodes, Zac Robinson, Mary Sanseverino, Chic Scott, Rain Scott, Stephen Slemon, William Snow, Karen Sorensen, Ellie Stephenson, Robert D. Turner, Nancy J. Turner, Robert Vranich, Kristen Walsh, Meghan J. Ward, Rob Watt, Cliff White, Andy Williams, Carmen Wong, Ken Wylie
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.379 | 0.138 |
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".