Why Socio-metabolic Studies are Central to Ecological Economics
Bibliographic record
Abstract
Global material extraction has tripled since the 1970s, with more than 100 billion tonnes of materials entering the world economy each year. Only 8.6% of this is recycled, while 61% ends up as waste and emissions that is the leading cause of global warming, and large-scale pollution of land, rivers, and oceans. This paper introduces Socio-metabolic Research (SMR) and demonstrates its relevance for ecological economics scholarship in India. SMR is a research framework for studying the biophysical stocks and flows of material and energy associated with societal production and consumption. SMR is widely conducted in Europe, US, and China. In India, it is still at an infant stage. In this paper, we review pioneering efforts of SMR in India, and make the case for advancing the field in the sub-continent. The crucial question is whether India can source materials and energy necessary for human development in a sustainable manner.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".