The Ethical Experience of Nature: Aristotle and the Roots of Ecological Phenomenology
Bibliographic record
Abstract
I demonstrate here how Aristotle's teleological conception of nature has been largely misunderstood in the scientific age and I consider what his view might offer us with regard to the environmental challenges we face in the 21st century. I suggest that in terms of coming to an ethical understanding of the creatures and things that constitute the ecosystem, Aristotle offers a welcome alternative to the rather instrumental conception of the natural world and low estimation of subjective experience our contemporary techno-scientific culture espouses. Among other things, I consider how his conception of orexis and eudaimonia (happiness or, as I prefer here, "the flourishing life") might be extended to include the eco-system itself, thus allowing us to better understand the moral meaning of nature. I conclude with a look at the way in which modern phenomenology re-addresses the fundamental Greek concern with ontology, meaning and human authenticity. I consider the ways in which phenomenology reasserts the value of direct human experience that was so important to Aristotle; and I consider how this view, and that of Deep ecology, may help us to experience nature - and all of Being for that matter - in a more authentic, meaningful and altogether ethical light.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.092 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 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".