Braided Learning : Illuminating Indigenous Presence through Art and Story : [book supplement]
Bibliographic record
Abstract
The Truth and Reconciliation Commission and Indigenous activism have made many Canadians uncomfortably aware of how little they know about First Nations, Métis, and Inuit peoples. In Braided Learning, Lenape-Potawatomi scholar and educator Susan Dion shares her approach to learning and teaching about Indigenous histories and perspectives. Métis leader Louis Riel illuminated the connection between creativity and identity in his declaration, “My people will sleep for a hundred years, but when they awake, it will be the artists who give them their spirits back.” Using the power of stories and artwork, Dion offers respectful ways to address challenging topics including treaties, the Indian Act, the Sixties Scoop, land claims, resurgence, the drive for self-determination, and government policies that undermine language, culture, and traditional knowledge systems. Braided Learning draws on Indigenous knowledge and world views to explain perspectives that are often missing from the national narrative. This generous work is an invaluable resource for Canadians trying to make sense of a difficult past, decode unjust conditions in the present, and work toward a more equitable future. The documents available here are a video of Susan D. Dion giving her historical timeline lecture (discussed in Chapter 3 of the book) and a colour supplement showing the artworks of the Indigenous artists featured in the book (Chapter 4). The artwork is shared by permission of the artists. [An updated version of the colour supplement with minor revisions was uploaded on 2024-05-06.]
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".