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Record W2525801727

Green Economy through the Rosia Montana Case - Best Solution in the Context of Schemes Offshore Routed by the International Corporations

2016· article· en· W2525801727 on OpenAlexaboutno aff
Nicolae Moroianu

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicNatural Resources and Economic Development
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Submarine pipelineBusinessEconomyEngineeringEconomicsGeographyArchaeologyGeotechnical engineering
DOInot available

Abstract

fetched live from OpenAlex

The object of researching paper, prepared by the student Nicolae Moroianu, under by Anişoara POPA, doc. conf. at University of Galati Lower Danube in analysis of the controversial case ” Roşia Montană - gold exploitation”. The utility of estimating needs for a structured analysis of the Roşia Montană case it is actually in Romanian society. Acording with the last 15 years, many conflicting tensions occurred between citizens, corporate officials, journalists, civil society actors and Presidential, Government and Parliament representatives. In this period, all stakeholders have provided often conflicting information and opinions on the benefits and risks in exploitation of gold and silver minerals from the Apuseni Mountains, by a Canadian majority-owned company. In 2016, mine opponents enjoyed a major victory when the village of Rosia Montana and surrounding Transylvania region were nominated to become a UNESCO World Heritage site, a designation protesters hope will secure international support and protection to the area. Still, the company continues to build the mine. Gabriel Resources is now threatening to sue the Romanian government under investment agreements for rejecting the mine. If they make good on this threat, the country could be embroiled in a World Bank tribunal trial for months.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.600
Threshold uncertainty score0.999

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 communication0.0010.002
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.241
GPT teacher head0.440
Teacher spread0.200 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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