Bibliographic record
Abstract
Abstract The ecological crisis has been traced to a rupture in the human-nature relationship, which sees the natural world as inert materials that serve human utility. This prevailing sense of separation is thoroughly embedded in Western culture through engrained metaphors that reinforce a view of the Earth as a subject of human mastery. To counter the disjuncture between humans and nature, some theorists have suggested a unitive view of nature, while others have argued for more expansive forms of identification that engender a more responsive ecological ethics. Despite these efforts, the human-nature dichotomy remains a perennial issue of debate, especially for environmental educators who strive to cultivate a more harmonious relationship with the earth. This article examines the Zen Koan (case or example) as a pedagogical innovation that hones the learner’s ability to entertain opposing propositions. Humans are both united withandseparate from nature at the same time. The Koan encourages an epistemological fluidity and openness to ambiguity that can enrich and deepen inquiry. In the context of environmental education, this contemplative approach to investigation can complement immersive pedagogies that enjoin somatic and sensory experience in explorations of the natural world.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.052 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.007 |
| 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".