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Record W3136860014 · doi:10.3389/feduc.2021.647764

Dancing With Non-duality for Healing Through the Shadows of the COVID-19 Pandemic

2021· article· en· W3136860014 on OpenAlexaff
Heesoon Bai, Kevin Berry, Jesse Haber, Avraham Cohen

Bibliographic record

VenueFrontiers in Education · 2021
Typearticle
Languageen
FieldPsychology
TopicPsychological Well-being and Life Satisfaction
Canadian institutionsAdlerCollege of New CaledoniaSimon Fraser University
Fundersnot available
KeywordsDuality (order theory)DualismConceptualizationEpistemologyDual (grammatical number)PandemicCoronavirus disease 2019 (COVID-19)PsychologyComputer sciencePhilosophyMathematicsMedicineArtificial intelligencePure mathematics

Abstract

fetched live from OpenAlex

The COVID-19 pandemic has unleashed torrents of global suffering at a devastating scale, necessitating a strong response to alleviating suffering. This paper begins with noting that the conventional approach to suffering in North America is to be positive and not to be negative. The paper summarily explores the philosophy of positive psychology underlying the first- and the second-wave of positive psychology, commenting on the evolution from dualism and a binary conceptualization in the first wave (PP 1.0) to a non-dualism of integrating binaries in the second wave (PP 2.0). PP 2.0’s enhanced therapeutic efficacy is noted for its non-dual framework. The paper then explores and suggests a different conceptualization possibility of non-duality, fundamental non-duality , that is related to but distinct from the one in PP 2.0. A case is made that fundamental non-duality has a radical possibility of therapeutic efficacy. Being consistent with the philosophy of non-duality, further suggestions are made that non-duality of PP 2.0 and fundamental non-duality can be therapeutically deployed together for greatest efficacy. The exploration contained in the paper is largely philosophical, arts-based, and autobiographical, creating an enacted and lived experience of applying theory to practice.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.242
Threshold uncertainty score0.211

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.052
GPT teacher head0.385
Teacher spread0.332 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2021
Admission routes1
Has abstractyes

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