A Study into the Demographics Having the Greatest Carbon Footprint
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Our aim was to determine factors predicting greenhouse gas emissions per capita and to identify the demographics having the greatest carbon footprint. The relationship between socioeconomic trends and greenhouse gas emissions is controversial, given that many past studies evaluated only a single factor. We analyzed the relationship between global greenhouse gas emissions per capita and literacy rate, GDP per capita, urban population percentage, adolescent fertility rate, unemployment percentage, percent of agricultural land, research and development expenditure, renewable energy consumption, food production, population growth, mobile cellular subscriptions, air transport freight, and forest area. We gathered data from 217 countries spanning a period of 20 years; 1993 to 2012. We analyzed the data using multiple regression models. We concluded food production, renewable energy consumption, air transport, mobile cellular subscriptions, literacy rate, and population growth have the greatest impact on predicting greenhouse gas (GHG) emissions in our model, suggesting the demographic with the greatest carbon footprint are wealthy, educated people living in urban centers.
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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 | 0.003 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it