Bibliographic record
Abstract
Ecopsychology provides numerous ways to define the boundary between self and environment, including the idea of an expanded self that incorporate the natural world into one’s self-perception. Within ecopsychology this concept is primarily approached from a neurotypical perspective. It is, however, valuable to consider the experience of Autistic individuals through this lens, particularly in relation to sensory experience. I propose that many Autistics experience a highly permeable self-environment boundary leading to self-identification that includes environment and heightened sensory input. I draw primarily from the ideas of Arne Naess (1995) on expanding spheres of self-identification and Theodore Roszak’s (1995) concept of the ecological unconscious to illustrate this. Additional support is provided by considering the lived experience of Autistic individuals, including the author. Very few published works examine Autistic experience as described by Autistics, which is critical in presenting an accurate perspective. By viewing Autistic sensory experience through the framework of ecopsychology and using the described experiences of Autistic individuals, the resulting conclusions can support more respectful and Autistic-affirming practices and supports.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.029 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".