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Record W2740768199 · doi:10.18533/rss.v2i7.106

Putting small-scale mining in perspective: an analysis of risk perception of a southwestern Nigerian community

2017· article· en· W2740768199 on OpenAlexaff
Sesan Adeniyi Adeyemi, Ayodele Olagunju

Bibliographic record

VenueReview of Social Sciences · 2017
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsScale (ratio)Ranking (information retrieval)Index (typography)PopularityPerceptionEnvironmental resource managementPopulationRisk perceptionBusinessEnvironmental planningGeographyEnvironmental healthPsychologyEconomicsMedicineSocial psychologyComputer science

Abstract

fetched live from OpenAlex

<p>Across the developing world, informal small-scale mining is still growing in popularity, so are its significant socio-environmental burdens to the host communities. The assumption that understanding risk tolerance at small scale of mining is important to recommending effective planning approach, in mitigating its environmental impacts, and in promoting environmentally responsible oversight gave rise to this survey. Quantitative responses from a systematic random sampling of 506 residents of Ijero-Ekiti mining community (southwestern Nigeria) are presented with an emphasis on risk perception and management measures. By ranking 21 variables identified through an extensive literature review, residents’ tolerance index (RTI) and resident agreement index (RAI) are computed. The results suggest that while residents’ risk perception is largely motivated by socio-economic considerations brought about by a major surge in population growth in recent years, there is greater desire for an active public engagement and improved regulatory oversight. The conclusion highlights the value of local capacity building and increased awareness of less risky economic alternatives in successfully implementing long-terms solutions to risks associated with unsustainable mining practice at any scale.</p>

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.226
Threshold uncertainty score0.407

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.039
GPT teacher head0.330
Teacher spread0.291 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2017
Admission routes1
Has abstractyes

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