Undoing human supremacy and white supremacy to transform relationships: An interview with Megan Bang and Ananda Marin
Bibliographic record
Abstract
Megan Bang (Ojibwe and Italian descent) is a Professor of the Learning Sciences and Psychology at Northwestern University and is currently serving as the Senior Vice President at the Spencer Foundation. Dr. Bang’s research focuses on the complexities of navigating multiple meaning systems in creating and implementing more effective and just learning environments in science, technology, engineering, arts, and mathematics education. Ananda Marin (African American, Choctaw [non-enrolled], European American descent) is an Assistant Professor of Social Research Methodology in UCLA’s Department of Education and faculty in American Indian Studies. Her research explores questions about the cultural nature of teaching, learning, and development. This interview with two Indigenous scholars provides educators with a chance to explore the possibilities of Indigenous worldviews on their climate change praxis. The scholars ask educators to consider how white and human supremacy are perpetuated in current educational paradigms. They discuss the necessity of transformations between relationships between humans and the natural world in fighting climate change. Bang and Marin underline the importance of education that immerses children in learning with places, paying attention to embodied, relational, axiological, and world-building dimensions of storying with lands.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.025 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.015 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".