EP#19 The Leanne Simpson Interview
Bibliographic record
Abstract
In this episode of Red Man Laughing we talk with author, scholar and artist - Leanne Simpson. Leanne is the author of the incredible "Dancing On Our Turtles Back" and having read the book (going on 12 times now), I decided that i NEEDED to have Leanne on the podcast to share her empowering and enlightening message. Her book changed my life and I not only wanted to thank her, but I wanted to dig a bit deeper in terms of decolinization and where that journey must start - with us, as individuals. As always, thanks for listening. Make sure you subscribe to the podcast to get new episodes send directly to your RSS Reader, Email or iTunes its FREE and easy to do and we certainly appreciate the support! BOOM! About Leanne Simpson Leanne Betasamosake Simpson is a writer and scholar of Michi Saagiik Nishnaabeg ancestry and is a member of Alderville First Nation. She holds a Ph.D. from the University of Manitoba, is an Adjunct Professor in Indigenous Studies at Trent University and an instructor at the Centre for World Indigenous Knowledge, Athabasca University. She has also lectured at Ryerson University, the University of Victoria, the University of Manitoba, and the University of Winnipeg. Leanne is also an oral story-teller and language-learner. She has performed at the last two Odemin Giizis festivals in addition to Nishnaabemowin Saswaansings Solstice Storytelling event. Dr. Simpson lives in Nogojiwanong, the inspiration for much of her work, where she homeschools her two children. She is currently the co-director of Wii-Kendimiing Nishinaabemowin Saswaansing, a language nest for Nishnaabeg families and she is also a member of OKaadenigan Wiingashk artist collective.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.031 | 0.007 |
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".