Rejecting the Colonial Politics of Recognition. Below the Radar podcast
Bibliographic record
Abstract
Glen Coulthard is Yellowknives Dene and is an associate professor in the First Nations and Indigenous Studies Program and the Department of Political Science at the University of British Columbia. He is also the author of the acclaimed book Red Skin, White Masks: Rejecting the Colonial Politics of Recognition from University of Minnesota Press. On this special episode live from the Vancouver Podcast Festival, host Am Johal sits down with Glen to talk about who and what influences his work and research, the different projects he’s been involved in over the years, and what continues to inspire him to do the work he does.\nRead more about Red Skin, White Masks: Rejecting the Colonial Politics of Recognition here: www.upress.umn.edu/book-division/b…kin-white-masks\nRead more about The Fourth World here: www.upress.umn.edu/book-division/b…he-fourth-world\nYou can read more about Glen Coulthard on our blog post: sfuwce.org/glen-coulthard/\nThis episode was recorded live at the 2019 Vancouver Podcast Festival, and we’re grateful to them for their invitation to be part of their programming at the Vancouver Public Library. To learn more about the Vancouver Podcast Festival, please visit their website: www.vanpodfest.ca/
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.017 | 0.015 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".