Linking the treadmills of production and destruction to disproportionate carbon emissions
Bibliographic record
Abstract
Our case-oriented analysis of the drivers of climate change highlights the barriers and pathways to climate action. We employ Qualitative Comparative Analysis (QCA), specifically fuzzy set Qualitative Comparative Analysis (fsQCA), to trace the ways in which combinations of climate change drivers helps us develop a better understanding of climate change, with a special emphasis on nation-states making a disproportionate contribution to this problem. World Bank data is used to analyze 179 countries allowing us to explore how population, gross domestic product per capita (Treadmill of Production), and military spending as a percentage of GDP (Treadmill of Destruction) provide different recipes for understanding two climate change outcomes as they relate to UN Sustainable Development Goals (particularly SDGs 10 and 13). Our analysis yields new insights, illuminating two distinct recipes, thereby indicating that a focus on mechanisms provides new ways of understanding social processes and their connection to carbon emissions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".