Education and ecological precarity: Pedagogical, curricular, and conceptual provocations
Bibliographic record
Abstract
Too big to imagine and too urgent to ignore, climate crisis is the text or the subtext of many of the news headlines as we write the editorial introduction to this special issue. We write while still in the COVID-19 pandemic, just after the COP26 climate summit in Glasgow, just after a summer of deadly heatwaves, just after a highway collapsed due to flooding in British Columbia, and just after the Royal Canadian Mounted Police again invaded Wet’suwet’en, where land defenders are engaged in the ongoing protection of their lands and waters from construction of a gas pipeline. No matter when you read this or where you are reading from, you will also be reading during and “just after” the devastation caused by climate crisis. We can count on the permanence of crises popping up, eroding away, and worsening. We are in times of guaranteed precarity. Youth climate activists continue to inspire; they hold corporations and governments to account for the lack of substantive action and bring attention to the need for action. Amidst the disappointments of the COP26 summit (including those identified by youth from all corners of the world 1 ) somehow scaling up and escalating a response to climate crisis remains ever more urgent. We are reminded of this urgency every day. A recent headline announced: “Extreme weather events are ‘the new norm’” ( McGrath, 2021 ). The article proceeded to name some of the extreme events of the year from around the world including drought, extreme rainfall, and an accelerated rise in sea levels. We are drowning in stories of ecological devastation, its disproportionately distributed effects, and colonial governments’ insistence on capitalist extractivism. The ruinous times illustrated by these stories demand urgent responses at multiple levels.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.039 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".