Bibliographic record
Abstract
“Self-realization is not a maximal realization of the coercive powers of the ego. The self in the kinds of philosophy I am alluding to is something expansive, and the environmental crisis may turn out to be of immense value for the further expansion of human consciousness. ” Arne Naess (In Drengson and Devall 2008, p. 132) We explore communication ecology and how it shaped Arne Naess’ development from child to mature scholar. As a nature-loving, cross-cultural, comparative philosopher, he explored cultures and nature together. His unified communication ecology reflected the places and cultural elements forming his character and professional approach. Both mirror the evolution of studying communication, cultures and languages with ecological methods used for comprehending the natural world. These methods focus on processes, relationships and whole systems, rather than reductionist analysis. Evolving communication and place-based learning systems are in living communities everywhere. There are cultures, language families, and dialects even in communities of nonhuman species. Naess studied world views and life philosophies as naturalists ’ study living species and beings in ecosystems. He explored classifying cultures, world views and religions with ecologically based methods. He called personal philosophies striving for ecological wisdom and harmony ecosophies. Communication ecology avoids the “one size fits all ” study of the world; it facilitates the wisdom of diversity in cultural, linguistic, technological and economic analysis. This approach is pluralistic rather than monolithic.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".