“Calling In” Ecopsychology: The Case of Ecotherapy and Nature-Based Education
Bibliographic record
Abstract
In this article, I argue that anticolonial, antiracist, and antioppressive foundations for Ecotherapy and Nature-based Education (ENBE) are an ethical and moral imperative. I identify such critical approaches as often inadequately developed within ENBE, which perpetuates harm and limits ENBE's capacity to respond to the tremendous social and environmental challenges of our time. In this context, I am Calling in those engaged in ENBE to work and grow together, to struggle and dwell in discomfort together, to compassionately hold each other accountable, to center and uphold the voices of those who have been marginalized due to systems of oppression and to do the vital work needed to dismantle oppressive structures and work toward justice and well-being for all life. In this article, I offer a series of reflections that unpack nuances, complexities, and problematics associated with some ENBE approaches with the intention of providing critical analysis, opportunity for contemplation and insight. I then provide suggestions and recommendations in the areas of relationality, accountability, and coresistance, working across cultural contexts and centering justice. The goal of this article is to work toward improving and transforming ENBE frameworks, programs, and practices so that ENBE can be more effective and responsive to this critical moment in history.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.121 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 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".