Regulating Water and War in Iraq: A Dangerous Dark Side of New Governance
Bibliographic record
Abstract
In the legal scholarship, the ‘new governance’ mode of governance advances an administrative arrangement where decision-making is shared amongst a range of actors, both public and private. The flexible, responsive, and collaborative governance orientation is intended to counter the ill effects of a coercive, top-down, state-centric, command-and-control approach to governance. Critics contend the new governance framework can displace the interests of local communities, disempower individuals, and dislodge basic human rights. The U.S. military has adopted such an adaptive approach in its own governance structure, which in this article is referred to as: the new governance “mentality.” This mentality of governance was employed in the U.S.’s post-conflict reconstruction efforts in Iraq—efforts that were plagued by waste, inefficiency, and corruption. Governance scholars have yet to ask the question of what models of governance should apply in the post conflict situation where the environmental violence of war has poisoned waterscapes and degraded landscapes. Should an adaptive mode of new governance be applied in post conflict situations where public institutions are weak and beset by corruption? What is the role of the state and private actors when the war is over and the reconstruction period begins? In this article, we explore a dark side of the new governance framework through the case study of the Iraq war theatre and examine how the transformed military culture shaped the 2003–2013 Coalition operations in Iraq and the reconstruction effort—in particular, the provision of safe, clean drinking water to local communities.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.059 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".