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Record W3084172955 · doi:10.1080/00083968.2020.1799830

Revisiting the host–refugee environmental conflict debate: perspectives from Ghana’s refugee camps

2020· article· en· W3084172955 on OpenAlexafffundvenue
Samuel K. M. Agblorti, Miriam Grant

Bibliographic record

VenueCanadian Journal of African Studies / Revue canadienne des études africaines · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change, Adaptation, Migration
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan Campus
FundersInternational Development Research Centre
KeywordsRefugeeNegotiationPoliticsResource (disambiguation)Host (biology)Political sciencePosition (finance)Development economicsEcologyBusinessBiologyEconomicsLaw

Abstract

fetched live from OpenAlex

In the debate over refugee–host community environmental conflicts, refugees are often blamed, premised on the notion that refugees add substantially to anthropogenic activities as a result of both their demographic and socio-economic status. We employ political ecology to understand how power and economic considerations play out in the access to and use of environmental resources in Ghana’s refugee-hosting communities. Drawing mainly on qualitative data generated through group discussions and in-depth interviews, we propose an alternative position that environmental conflicts are driven by the inability of hosts to fulfil their economic interests from refugee activities. Where such economic interests are fulfilled, host–refugee environmental interactions are more likely to be devoid of conflicts even where environmental deterioration is pronounced. Negotiating how hosts and refugees collaborate in the use of, and returns from, environmental resource-related activities holds a central position in stemming environmental conflicts.

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)0.011
metaresearch head score (Gemma)0.010
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 score0.020
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0200.031
Scholarly communication0.0090.012
Open science0.0020.009
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0050.000

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 head0.085
GPT teacher head0.277
Teacher spread0.191 · 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

Citations10
Published2020
Admission routes3
Has abstractyes

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Same venueCanadian Journal of African Studies / Revue canadienne des études africainesSame topicClimate Change, Adaptation, MigrationFrench-language works237,207