Seminar 7: Dr. Sharon Stein (University of British Columbia, Canada) - Teaching and Learning for the End of the World as We Know It
Bibliographic record
Abstract
In this talk, Dr Stein will share some of the pedagogical work of the Gesturing Towards Decolonial Futures Collective, which invites people to expand their capacity to face painful realities about the climate and nature emergency (CNE) and its colonial root causes in intellectually discerning, relationally mature, and intergenerationally responsible ways. Education related to the CNE is often treated as an informational problem; we believe that if people only knew the “facts”, they would change their behaviour. But what if the CNE is not the result of a lack of information, but the product of enduring investments in modernity’s inherently violent and unsustainable habit of being? What if we need to disinvest from and mourn the end of that mode of existence so that something else can become possible? What kind of education could prepare us to face “the end of the world as we know it” without throwing up, throwing a tantrum, or throwing in the towel? Bio: Dr Sharon Stein is a white settler scholar and Associate Professor in the Department of Educational Studies at the University of British Columbia. She is the author of the book Unsettling the University: Confronting the Colonial Foundations of US Higher Education, founder of the Critical Internationalization Studies Network, and one of the co-founders of the Gesturing Towards Decolonial Futures Collective.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.147 | 0.033 |
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".