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Record W2001837363 · doi:10.1080/15423166.2013.785651

Peace and Pollution: An Examination of Palestinian—Israeli Trans-Boundary Hazardous Waste Management 20 Years after the Oslo Peace Accords

2013· article· en· W2001837363 on OpenAlexaff
Ilan Alleson, Jamie Levin, Shmuel Brenner, Mohammad Said Al Hmaidi

Bibliographic record

VenueJournal of Peacebuilding & Development · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicMiddle East Politics and Society
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsHazardous wasteNegotiationPeacebuildingEnvironmental planningPolitical scienceBoundary (topology)Conflict managementPoliticsEnvironmental securityMilestoneState (computer science)Public administrationLawEngineeringGeographyWaste management

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0080.007
Scholarly communication0.0060.003
Open science0.0000.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.015
GPT teacher head0.264
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2013
Admission routes1
Has abstractyes

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