Buddhist Resources in the Race Against Global Heating: Beginner’s Mind as an Antidote to Misguided Certainty and the Status Quo
Bibliographic record
Abstract
Indigenous scholar Kyle Whyte notes the limitations of embracing a “crisis epistemology” which seeks a clear, instrumentalist solution to a problem based on conceits of certainty rather than embracing an integrated view of ecological complexity with an eye toward genuine restoration, sustainability, and justice. If crisis epistemologies lack creative and nuanced engagement with possible futures they easily perpetuate, if unintentionally, destructive colonial industrial-growth paradigms that have brought humans into conflict with the biosphere. This essay explores the problem of certainty and the way that Buddhism offers an alternative to colonialist “crisis epistemology.” The value of uncertainty is manifest in Buddhism as a recognition of the nature of saṃsāra as marked by impermanence, dukkha, and no-self (the trilakṣaṇa) and comes out explicitly with the “don’t know mind” (buzhi xin 不知心) of the Chan tradition. I examine the application of these ideas within contemporary engaged Buddhist communities, considering how they have seized upon uncertainty as exemplified in “don’t know mind” to generate “wise hope” or “active hope” and move beyond despair into effective activism. I argue that Buddhist comfort with uncertainty provides an antidote to the instrumentalist conceit of certainty and opens space for transformative activism.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.010 | 0.037 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".