Bibliographic record
Abstract
This episode was produced by Mendel Skulski and Adam Huggins, with help from Eric Higgs, and features the voices of Jeanine Rhemtulla, Eric Higgs, Mary Sanseverino, Brian Starzomski, Bill Snow, Sandra Frey, Julie Fortin, Jenna Falk, Alina Fisher, Andrew Trant, Kristen Walsh, Jill Delaney, and Rob Watt. Music by Thumbug, Shadow Acid, Erik Tuttle, Sage Palm, and Sunfish Moon Light From Eric: There are many voices associated with the Mountain Legacy Project, and only a few are represented in this episode. First, to the gifted field team members, who since 1998 greeted so many mountains and learned to love delicate camera equipment. Dedicated volunteers such as Ian MacLaren, Rob Watt, Rick Arthur and Sandy Campbell turned a research notion into a sprawling project. Heroes within institutions such as the University of Victoria, Parks Canada, Alberta Agriculture and Forestry—Jonathan Bengston, Shahira Khair, Jeff Albert, Rick Kubian, Mike Eder, Kim Pearson, Bruce Mayer and many others—ensured the flow of in-kind support and funding. Funding agencies, such as SSHRC and fRI Research, supported innovation. Colleagues and students near and far have continuously stirred new ideas. All of this work is made possible by two professionally introverted groups: archivists and software specialists. Thank you to Pascal LeBlond, Jill Delaney and the digitization team at Library and Archives Canada, and software whizzes Chris Gat, Spencer Rose and Mike Whitney. Might the most enduring legacy of our work be the transfiguration of colonial materials for support of Indigenous resurgence and reconciliation? I hope so, and am grateful for colleagues and advisors including Sarah Hunt, Darcy Mathews, Ry Moran, Craig Richards, Bill Snow and others for showing the way, such as the Canadian Mountain Network, SSHRC and fRI Research. Funding for this episode of Future Ecologies was granted through the University of Victoria’s Pathways to Impact fund.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.220 | 0.041 |
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".