Bibliographic record
Abstract
States rich in natural resources often have poor human rights records. Political scientists have labelled this correlation the “resource curse”. To address structural causes and the misappropriation of resources by the rulers of those countries, Leif Wenar proposes in his book, Blood Oil, the so-called Clean Trade Act. We draw on Wenar’s work to theorise how activists interested in bringing about these changes might utilize the existing features of international law to adopt Wenar’s proposals. Activists interested in taking up Wenar’s challenge to reform the ownership and distribution of global commodities would move beyond Wenar’s narrow public law framework, and study the myriad forms of regulation and interactions that define the contemporary transformation of political sovereignty and rule-making under the conditions of globalisation. It is not enough that Clean Trade Laws are passed in developed nations.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.312 | 0.128 |
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".