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Record W4410220793 · doi:10.15239/ycjcb.01.02.09

Buddhist Resources in the Race Against Global Heating: Beginner’s Mind as an Antidote to Misguided Certainty and the Status Quo

2024· article· en· W4410220793 on OpenAlexaff
Brian J. Nichols

Bibliographic record

VenueYin-Cheng Journal of Contemporary Buddhism · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicIndian and Buddhist Studies
Canadian institutionsMount Royal University
Fundersnot available
KeywordsStatus quoBuddhismCertaintyAntidoteRace (biology)PsychologyPolitical scienceEpistemologyPhilosophySociologyLawTheologyMedicineGender studies

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.822
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.033
GPT teacher head0.277
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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