Bibliographic record
Abstract
When people think of bioregionalism they often envision of watersheds, biotic zones, fauna and flora types, weather patterns, decentralization, and autonomy. Intriguingly, in his seminal essay “Living by Life: Some Bioregional Theory and Practice,” bioregionalist Jim Dodge (1981) cites “spirit places” as the third tenet of bioregionalism. However, aside from mentioning Mt. Shasta and the Pacific Ocean as “psyche-tuning power-places,” he offers little in the way of how spirits places shed light on bioregionalism. This paper, drawing on two research trips among the highlanders of Northeast Cambodia, concludes that a bioregional theory that does not include spirit places is myopic. Taken in the highlander context in Cambodia, such a theory is utterly meaningless. The Brao, Tampuan, Bunong and other ethnic minorities of Ratanakiri province show us that bioregionalism is more than geographical separation points and intimate knowledge of local ecology; knowing where spirits dwell and how to deal with them is an equally and quite possibly more important aspect of living sustainably and fruitfully in one’s bioregion. Numerous environmental writers have put forth that indigenous people worldwide are our best teachers for living symbiotically with nature, yet most of these authors are selective students; they are interested in biology lessons, but not spiritual ones. But biology and spirituality are not separate entities for the highlanders of Northeast Cambodia; they are one in the same. This paper argues the point that bioregionalism must take in account spiritual dimensions of the landscape as pointed out by indigenous people.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.041 |
| Scholarly communication | 0.009 | 0.029 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.013 | 0.028 |
| Insufficient payload (model declined to judge) | 0.004 | 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".