“Skin contains land and birds”: Understanding inner healing intelligence through critical vitalism and Indigenous thought
Bibliographic record
Abstract
Abstract Inner healing intelligence (IHI) is a foundational orienting concept in the psychedelic-assisted therapy (PAT) field that refers to the innate tendency of living beings to move towards healing. In this paper, we introduce an expanded articulation of IHI, drawing largely on vitalism and Indigenous philosophy from the Americas. We conceptualize IHI as the innate capacity of an individual to move towards healing by engaging with the vital life force of existence specific to place and intrinsic to the myriad more-than-human relationships that constitute the extended self. Rather than presenting a prescriptive framework, our aim is to invite the PAT community to take IHI seriously and to imaginatively explore the implications of this expanded view. We offer this articulation not to define or delimit the concept, but to contribute to a broader, ongoing conversation about the relational and ecological dimensions of healing. By foregrounding the ontological and ethical consequences of IHI, we suggest that this perspective can enrich therapeutic practice and support the collective aspirations of the psychedelic renaissance. To this end, we propose several recommendations for how a more emplaced, embodied, and relational enactment of IHI might unfold in practice, while pointing to future directions for inquiry that resists closure.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.059 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".