In Conversation with Steven Khan: Sensible and Sense-able Qualitative Literacies for Multi-species Flourishing
Bibliographic record
Abstract
Abstract In this conversation with Steven Khan, he illuminates his recent trajectory toward multi-species flourishing with mathematics education. Incisively including colonial plantation logics into contemporary conversations of the Anthropocene in STEM education, Khan sketches out a figuration of mathematics education that continues to uphold, albeit differently, a political economy rooted in consumption, overwork, uncompensated labor, scarcity, violence, and erasure. In response, and recognizing that no human flourishing occurs without the flourishing of other-than-human kin, Khan offers multi-species flourishing. To animate multi-species flourishing, Khan offers examples such as critical examinations of sonic landscapes as novel means of attuning otherwise, as well as how it might become an actionable practice in spaces such as mathematics teacher education. Khan concludes this conversation by highlighting the importance of creating collectives in which differences are assets rather than liabilities and are treated as such.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".