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Record W2105141127 · doi:10.1144/gsl.sp.2005.250.01.04

The role of minerals in sustainable human development

2005· article· en· W2105141127 on OpenAlexaff
Jeremy P. Richards

Bibliographic record

VenueGeological Society London Special Publications · 2005
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicNatural Resources and Economic Development
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSustainable developmentBusinessGeochemistryEnvironmental planningGeologyEnvironmental scienceBiologyEcology

Abstract

fetched live from OpenAlex

Abstract Sustainable mineral resources development can be seen as the equitable conversion of transient mineral wealth into durable social and environmental capital. In the past, this conversion has not been efficient or equitable, with benefits accruing mainly to First World investors and consumers by externalization of social and environmental costs to local people and places. Modern industry, led by large multinational corporations, is in the process of changing its modus operandi to embrace ideas of corporate and social responsibility. The damage from past practices to the developing world is severe, however, and may require measures beyond voluntary or current legal instruments to reverse degenerative trends. Central among these requirements is Third World debt cancellation. However, the mining industry can also contribute by fully internalizing the costs of mineral production, and paying a fair price for the resources it extracts; these internalized costs should be reflected in higher commodity prices. This can be achieved through a combination of financial instruments and incentives, innovation, and best practice, with essential consumer buy-in through increased awareness.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.009
Scholarly communication0.0040.003
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.020
GPT teacher head0.222
Teacher spread0.202 · 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 designTheoretical or conceptual
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

Citations9
Published2005
Admission routes1
Has abstractyes

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