Proxy-led accountability for natural resource extraction in rentier states
Bibliographic record
Abstract
The resource curse literature suggests that, in fragile states dependent on natural resource rents, structures of public accountability are weak because of an elite-controlled political economy indifferent to social and ecological interests. We examine accountability claims made by non-domestic proxy actors, holding governments and corporations accountable on behalf of communities adversely affected by natural resources extraction. This conceptualization is suggested by proxy-led transnational mobilization against mining-related damage in the Democratic Republic of the Congo. We identify an ‘hourglass’ structure of proxy actor engagement with affected communities: In a first phase, proxies rely on public mechanisms to define standards remotely. In a second phase, proxies ‘narrow’ the gap by seeking compliance information from affected communities. However, in a third phase, this gap ‘widens’ again when proxies remotely seek sanctions against responsible actors. We discuss the applicability of this heuristic framework to proxy-led accountability practices in other natural resource-dependent rentier states.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".