Green Healthcare – Collective Wellbeing for People and Planet
Bibliographic record
Abstract
Background: As global climate change accelerates, the crisis of species survival invites holistic ways of knowing. There is a resurgence of engagement in Indigenous spiritual wellbeing systems as part of anti-colonial liberation movements. Green collective wellbeing systems (GreenCoWell) offer opportunities to heal both people and the planet, addressing the notion of separation between life forms. Objective: We plan to study and elaborate upon 6 BIPOC health practices based on interconnection, including family constellation healing (Zulu nation, Southern Africa), fa (Ghana), yoga (India), shinrinyoku (Japan), Danza Azteca (Central America), and one practice to be identified in the course of the study. From a feminist, anti-racism and decolonial lens, our work aims to support ways of knowing which originate from the Global South and Indigenous communities. Methods: Applying a participatory action research approach, we will blend qualitative and arts-based methods to portray 6 global GreenCoWell. Healers from each tradition will be interviewed separately and will engage in a collective dialogue on the desire, need, and methods for proliferating GreenCoWell systems. Results: The results of this project will be a film, poems, stories, academic products, social media messages, and a manifesto emanating from the collective dialogue. Conclusion: This mixed methods arts-based, feminist, anti-racism, and decolonial project brings together healers from 6 traditions, representing a novel approach to addressing climate change. Those who practice GreenCoWell engage in environmental conservation. Our long term aspiration is for more people to experience mental, physical, and spiritual wellbeing through these and related GreenCoWell and take action for climate justice.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.019 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".