Redrawing Relationalities at the Anthropocene(s): Disrupting and Dismantling the Colonial Logics of Shared Identity Through Thinking with Kim Tallbear
Bibliographic record
Abstract
Abstract What does it mean to respond to the Anthropocenes, plural, when doing science education? Specifically, can we critically engage with the Anthropocene, singular, without responding to the multiplicity in which Indigenous land and its many facets within the global community were at risk of destruction from Man? In this work, we contemplate the urgency of the inclusion of Indigenous philosophies and ways-of-knowing within the arching body politic, giving space to these practices that have been otherwise silenced within and beyond Western colonial frames. We argue that if the ways of thinking and practicing science and science education continue to stem from settler colonialism, capitalism, and toxicity, having previously and continually been responsible for the erasure of Indigeneity, the response within the Anthropocene will be multitudinously harmful. Here, we turn to Dakota scholar, Kim Tallbear, (Native American DNA: Tribal belonging and the false promise of genetic belonging, University of Minnesota Press, 2013) and her work in the intersections of identity, science, settler relations, and Indigeneity with the use of provocative imagery to the innate feeling of and within the Anthropocene(s).
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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 | 0.005 | 0.004 |
| 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 | 0.073 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".