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Record W3180873935 · doi:10.35188/unu-wider/2021/059-7

Promoting environmental sustainability in the artisanal and small-scale mining sector in Tanzania

2021· report· en· W3180873935 on OpenAlexaff
Abel Kinyondo, Chris Huggins

Bibliographic record

VenueWorking Paper Series · 2021
Typereport
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsTanzaniaScale (ratio)BusinessSustainabilityRevenueStakeholderEnvironmental planningEnvironmental resource managementGeographyPolitical scienceEconomicsPublic relationsFinanceEcology

Abstract

fetched live from OpenAlex

This study examines the interaction between formalization of the artisanal and small-scale mining subsector and the regulation of negative environmental impacts in Tanzania. Formalization generally seeks to move the artisanal and small-scale mining subsector to legal status. Using documents, reviews, and interviews with key informants, the study suggests that there is generally no automatic connection between formalization of artisanal and small-scale mining and improvement of environmental protection in Tanzania. The reasons behind this situation include lack of funding, limited capacity, poor coordination, the nomadic nature of artisanal and small-scale mining operations, and using formalization as a revenue-increasing tool. To address this situation, we propose the formation of a well-funded umbrella body that only deals with the artisanal and small-scale mining subsector and formulation of an artisanal and small-scale mining-specific legal framework that delineates responsibilities of different artisanal and small-scale mining regulatory agencies to avoid overlaps and gaps. Moreover, the capacity of artisanal and small-scale mining associations should be strengthened to enable them to promote environmentally sustainable practices and engage meaningfully in stakeholder consultation meetings.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.518
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.014
GPT teacher head0.203
Teacher spread0.189 · 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 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

Citations4
Published2021
Admission routes1
Has abstractyes

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