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Record W1598209235 · doi:10.25071/1920-7336.21280

Reducing Environmental Damage Caused by the Collection of Cooking Fuel by Refugees

2002· article· en· W1598209235 on OpenAlexvenueno aff
Maureen Lynch

Bibliographic record

VenueRefuge Canada s Journal on Refuge · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsRefugeeScope (computer science)Agency (philosophy)PovertyEnvironmental planningNatural resourceBusinessNatural disasterEnvironmental resource managementNatural resource economicsEnvironmental economicsPolitical scienceEconomic growthEconomicsGeographyComputer scienceSociology

Abstract

fetched live from OpenAlex

I The collection of fuelwood by large numbers of internally displaced people and refugees for the purpose of providing energy for food preparation and cooking can cause environmental devastation and adversely affect the socio-economic balance with local populations. There is no simple solution. Reducing environmental impact, and thus easing societal tensions, requires addressing a complex set of issues including supply of and demand for natural resources, aid agency operations, willingness to utilize refugee knowledge and experience, the effects of forced displacement, poverty, and lack of land. The key to establishing sustainable solutions, whether fuel or non-fuel alternatives, requires being able to identify and understand the interaction between human needs and behaviour and the local environment. This paper explores the scope of the problem and offers case examples, describes efforts taken and alternatives available, presents outcomes of evaluations that have been performed, and outlines lessons learned to be used in future crises.abstract

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.447
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.007
GPT teacher head0.191
Teacher spread0.184 · 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

Citations17
Published2002
Admission routes1
Has abstractyes

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