A Quantitative Approach to Evaluating International Environmental Regimes
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
Quantitative analysis of environmental regime effects can complement qualitative analyses by allowing investigation of variation in the effects of different regimes as well as the causes and conditions that explain that variation. Such analysis involves developing metrics that allow comparison of the influence of disparate regimes, models that distinguish regime influence from other explanatory factors, and data sets of independent and dependent variables of sufficient quality to support quantitative analysis. The many theoretical, methodological, and empirical obstacles to undertaking quantitative research on regime effectiveness are daunting but surmountable. By using data regarding component parts of regimes (“subregimes”) broken down to the country and year level, quantitative techniques offer promise in identifying which regimes induce greater behavioral change and greater “effort” and, more importantly, what characteristics of those regimes and the context in which they operate explain their greater success.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.007 |
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