Dancing With Non-duality for Healing Through the Shadows of the COVID-19 Pandemic
Bibliographic record
Abstract
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.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.081 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".