From the Anthropocene to Mutual Thriving: An Agenda for Higher Education in the Ecozoic
Bibliographic record
Abstract
Higher education in the global North, and exported elsewhere, is complicit in driving the planet’s socio-ecological crises by teaching how to most effectively marginalize and plunder Earth and human communities. As students and activists within the academic system, we take a firm stand to arrest this cycle, and to redirect education toward teaching how to create conditions for all life to thrive. In this paper, we articulate a research and education agenda for co-constructing knowledge and wisdom, and propose shifts in the ‘ologies from the current, destructive modes to intended regenerative counterparts. We offer to shift from an ontology of separation to that of interconnectedness; from an epistemology of domination to that of egalitarian relationship; and from an axiology of development to that of plural values for world- and meaning-making. Such paradigm shifts reflect the foundational aspirations of the consilient transdiscipline of ecological economics. We analyze several introductory university textbooks in economics, law, and natural sciences, to demonstrate how destructive ‘ologies are taught in North American universities, and how such teaching implicitly undermines critical inquiry and effective challenge. Our strategy for change is to provide a new theoretical framework for education: the regenerative ‘ologies of the Ecozoic’, based on biophysicality, embedded relationality, pluralism, and the sustainable well-being of all members in the community of life.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.045 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".