Cultivating Pearls of Wisdom: Creating Protected Niche Spaces for Inner Transformations amidst the Metacrisis
Bibliographic record
Abstract
The impetus for this paper emerges from the growing interest in leveraging inner transformations to support a global shift in ways of seeing and being. We caution that without sufficient individual and systemic maturity, inner transformations will be unable to hold the whole story and that attempts to drive paradigmatic shifts in ill-prepared systems will lead to insidious harms. As such, interventions for inner change will not have sufficient protected niche space to move beyond the boundaries of best practices towards wise practices. Drawing on Indigenous trans-systemics, we offer the metaphor of pearls as an invitation to recontextualize how inner transformations are conceived and approached in the metacrisis. To further develop this notion, we share a story of Wendigo and Moloch as a precautionary tale for the blind pursuit of inner and outer development. Weaving together metaphor, story, and scientific inquiry, we bring together Anishinaabe and Western knowledge systems for the purposes of healing and transformation. We hope that this paper will create space for wise practices—gifts from Creator to help sustain both Self and the World—to emerge, establish, and flourish. We invite readers on an exploration into the whole system of systems that are endemic to Anishinaabe cosmology, and a journey of reimagining new stories for collective flourishing amidst the metacrisis.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.065 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".