Rewilding Education in Troubled Times; or, Getting Back to the Wrong Post-Nature
Bibliographic record
Abstract
The first part of this paper provides a series of conceptual critiques to illustrate how the recent move to inaugurate a “post-nature” world works to vindicate anthropocentric perspectives and a techno-managerial approach to the environmental crisis. We contend with this premise and suggest that troubling nature has profound implications for education. In the second part, we provide case studies from nature-based programs in The Netherlands and Canada to demonstrate how anthropocentric thinking can be reinscribed even as we work towards “sustainability.” Despite the tenacity of human hubris and the advent of the Anthropocene, we suggest these troubled times are also rich with emerging “post-anthropocentric” perspectives and practices. As such we offer “rewilding” as a means to think about education that moves beyond the romantic vestiges of “Nature” without lapsing into delusions of human exceptionalism.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.076 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".