Bibliographic record
Abstract
Understanding the concept of interdependence means acknowledging myriad variables that assemble, separate, and redistribute in a virtual infinitude of relations. These interdependent variables, and the relations between them, can be seen as becoming perceptibly more abundant and volatile when viewed at finer, local, levels of scale. Conversely, they become simpler and more stable when viewed at cruder or macro-levels of scale. The question for policy and other practical applications, then, is which level of scale is most appropriate when dealing with any given interdependent phenomenon? Is it necessary to proceed with less "true" pictures at a cruder scale in order to foster more pragmatic results? These issues are explored through an analysis of the formulation and implementation of human wildlife conflict (HWC) policy in Bhutan. This exploration demonstrates that while HWC policy formulated at the macro-level contains an explicit focus on interdependence, when the policy is implemented at local levels of scale, much more complex tangles of interdependence emerge. These tangles obfuscate perceptions of the cause of HWC and, for some, drive opposition to a key value that is a foundation of the HWC policy. Drawing on the Bhutanese case, we advance ideas on how public policy and educational contexts can practically respond to the challenge of interdependence at different levels of scale.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".