Peace and Pollution: An Examination of Palestinian—Israeli Trans-Boundary Hazardous Waste Management 20 Years after the Oslo Peace Accords
Bibliographic record
Abstract
As part of the Oslo Accords, Israel and the Palestinian Authority agreed to jointly manage issues of environmental concern according to internationally recognised standards. The purpose of this paper is to qualitatively evaluate the outcomes of the Palestinian–Israeli Oslo environmental peace agreements regarding trans-boundary hazardous waste management. Hazardous waste is an area of particular importance given the potential for inefficient management to impact on public health and shared ecological resources. Although the environmental negotiations that took place within the framework of the Oslo Accords can be seen as a significant milestone for environmental cooperation, many objectives were never achieved. Ultimately, both parties were left with suboptimal trans-boundary management, in practice, because broader political disputes derailed cooperation in many technical spheres. This outcome can be attributed to four main factors: Israeli security concerns, territorial disputes, logistical ambiguities and Palestinian institutional constraints. The outcomes of the environmental agreements challenge neo-functionalist approaches to peacebuilding at the inter-state level. Given the risks environmental concerns pose to both sides, new models are needed that disentangle the management of immediately shared environmental challenges from the ongoing conflict.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".