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Record W2970496064 · doi:10.1080/08941920.2019.1657995

To Move or not to Move: Community Members’ Reaction to Surface Mining Activities in the Upper West Region of Ghana

2019· article· en· W2970496064 on OpenAlexafffund
Roger Antabe, Kilian Nasung Atuoye, Vincent Kuuire, Yujiro Sano, Godwin Arku, Isaac Luginaah

Bibliographic record

VenueSociety & Natural Resources · 2019
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsUniversity of TorontoWestern University
FundersCanada Research Chairs
KeywordsContext (archaeology)CentralityGold miningGeographySurface miningEnvironmental planningCommunity participationEnvironmental resource managementSocioeconomicsEnvironmental protectionBusinessEnvironmental scienceCoal miningSociologyArchaeology

Abstract

fetched live from OpenAlex

Despite the impact of mining-induced environmental change on community livability, we know little about how disparities in knowledge of health risks associated with mining influence residents’ response, especially in an already environmentally stressed context. Guided by theoretical insights from solastalgia, we examined residents’ decision to relocate due to increasing gold mining activities in the fragile Northern Savannah Ecological Zone of Ghana. Fitting complementary log-log regression models to cross-sectional data from the Upper West Region (UWR) of Ghana, we found that residents with limited knowledge of potential health impacts of mining and those who believe mining activities were not meeting environmental standards were more likely to consider relocating. Given the centrality of land in community health and wellbeing in the UWR, Ghana’s mining guidelines should promote local participation in the regulation of mining activities and guarantee the rights of indigenes to livable native lands.

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)0.001
metaresearch head score (Gemma)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.017
GPT teacher head0.248
Teacher spread0.231 · 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

Citations16
Published2019
Admission routes2
Has abstractyes

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