Depression and the natural world: towards a critical ecology of psychological distress
Bibliographic record
Abstract
Researchers have struggled to explain the dramatic increase in diagnoses of ‘depression’ in the industrialised world. This paper argues that psychological distress is likely to arise within an ecological context that is becoming increasingly degraded, and in which the character of selfhood is being redefined to fit an industrialised context. In turn, these redefinitions of selfhood reduce our capacity to address ecological concerns. I argue that it is only possible to recognise the connections between human well-being and ecological health if we identify and challenge the dissociations and repressions on which the ‘business as usual’ of industrial society depends, and that a more embodied conception of the person is fundamental to this recovery of our wholeness. More specifically, I argue that our current reliance on cognition and our corresponding marginalisation of sensing and feeling, in addition to undermining human well-being, may be ecologically catastrophic.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.007 | 0.124 |
| Scholarly communication | 0.020 | 0.021 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.006 | 0.013 |
| 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".