Happy Environments: Bhutan, Interdependence and the West
Bibliographic record
Abstract
There is a growing trend to understand economic and environmental policies in terms of multiple dimensions and “interdependence.” Bhutan is increasingly seen as an operational model with its Gross National Happiness (GNH) strategy. GNH, which is rooted in Mahayana Buddhism, is a framework and set of policy tools that conceptualizes sustainability as interdependent ecological, economic, social, cultural and good governance concerns. Bhutan’s practical GNH experience illustrates a significant ability to positively couple economic growth with a healthy environment. Can the “West”—with its legacy of either/or economics—learn anything from Bhutan’s multidimensional policy experiment? At first, it would seem not. It is questionable whether the West can replicate Bhutan’s unorthodox policy tools as we do not have a balancing set of Buddhist values rooted in mainstream culture. We are not equipped to respond to the many unintended consequences of interdependent policy because we do not yet understand what “interdependence” actually entails. There is hope, but much of it exists in the grey literature of ecological economics. This literature is in urgent need of greater exposure if we are to imagine and enact sustainability policy tools that are truly sensitive to interdependence, and thus follow Bhutan on its perilous but necessary journey.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".