A Review of Anna-Leah King, Kathleen O’Reilly, and Patrick J. Lewis’ (Eds.) (2024) <i>Unsettling Education: Decolonizing and Indigenizing</i> <i>the Land</i>
Bibliographic record
Abstract
Unsettling Education: Decolonizing and Indigenizing the Land (King, O’Reilly, & Lewis, 2024) focuses on decolonization, Indigenization and reconciliation for educators and students through addressing colonialism within the education system and academia. The authors each bring forth an abundance of knowledge and experience in the field of Indigenous education through the stories and words of Indigenous communities, Elders, Indigenous leaders, and Knowledge Keepers. Further, the authors provide an abundance of examples/teachings regarding Indigenous perspectives, theories, and teachings for current and future teachers to implement in the classroom. In addition, this book serves as a guide for non-Indigenous peoples to practice self-reflexivity, by reflecting on their own positionality and privilege while engaging with truth and reconciliation strategies through the stories and experiences of Indigenous scholars and educators. The book is comprised of eighteen chapters by twenty-nine authors (both Indigenous and non-Indigenous) and grouped into three sections.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".