Bibliographic record
Abstract
Why did the modern world enter into a “great forgetting” about the more-than-human world so many indigenous peoples took for granted? Second, how can this previous knowledge be reacquired without rejecting the very real accomplishments of the modern mentality? Many deep ecological writers have done extraordinary work on this second question. I will focus on the first, and use its analysis to add some insights regarding the second. Central to the argument I will make is how language both empowers us and to some degree separates us from direct experience of the other-than-human world. Western languages are particularly prone to reinforcing this separation. Equally central will be a discussion of how media of communication rooted in language further distances us from direct encounter. Also important will be work in contemporary biology and ecology exploring how deeply interconnected all life forms are. The traditional Western idea of individuals, be they plants and animals or human beings, are ultimately irreducibly distinct from their environment has been shown to be mistaken. Individuals have been shown to be made up of simpler individuals who, in relationship with one another, enable emergent qualities to arise at ever greater levels of complexity. Further, while genuinely individual, they cannot be understood without reference to relationships outside what are normally considered individual boundaries. By seeking the foundations of morality and other values in theology, reason, or will, many moderns are blinded to the fact values supporting morality and beauty exist immanently within the natural world. There is no need to import them from elsewhere. By way of conclusion, I reverse direction and describe one method available to the reader how a ‘remembering’ can come about experientially. This remembering will reconnect with an indigenous and sometimes shamanic perception of the world as alive and connected.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.033 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".