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Record W7124251210 · doi:10.5281/zenodo.18260189

Management of spatially extensive natural resources in postwar contexts: working with the peace process

2009· article· W7124251210 on OpenAlexaff
Jon D. Unruh, Julia N. Bailey

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2009
Typearticle
Language
FieldSocial Sciences
TopicTransboundary Water Resource Management
Canadian institutionsMcGill University
Fundersnot available
KeywordsNatural resourceProcess (computing)Work (physics)Resource (disambiguation)Natural (archaeology)Resource management (computing)

Abstract

fetched live from OpenAlex

While extensively occurring natural resources play a fundamental role in the survival and recovery of postwar populations, their management is not presently part of the operational priorities in a peace process. Dependence on naturally occurring food, fuel, water, secure locations, and products that can be obtained and sold quickly for dislocated, war-weary populations is a primary approach to postwar livelihoods. The peace process however focuses on the logistical and institutional aspects of security, demobilization, reintegration and humanitarian efforts. The result is profound degradation of the spatially extensive resources necessary for longer-term recovery. The primary reason for the inattention to resource degradation in a peace process is that conventional conservation approaches do not fit with the priorities of a peace process or attend to the immediate needs of a postwar population; designed as they are for stable, peaceful settings. This article focuses on the need to derive postwar natural resource management approaches which can work with the in-place priorities of a peace process. Four such approaches are suggested, with successful examples from specific countries.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.939
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0030.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.249
Teacher spread0.227 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations0
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)Same topicTransboundary Water Resource ManagementFrench-language works237,207