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Record W2805463235 · doi:10.1017/s1355770x18000153

Effects of ups and downs of the Mongolian mining sector

2018· article· en· W2805463235 on OpenAlexfundno aff
Tsolmon Baatarzorig, Ragchaasuren Galindev, Hélène Maisonnave

Bibliographic record

VenueEnvironment and Development Economics · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicNatural Resources and Economic Development
Canadian institutionsnot available
FundersDepartment for International DevelopmentInternational Development Research CentreGovernment of Canada
KeywordsProsperityEconomicsBoomDutch diseaseAgricultureUnemploymentComputable general equilibriumNatural resourceNatural resource economicsEconomyMonetary economicsMacroeconomicsEconomic growth

Abstract

fetched live from OpenAlex

Abstract The economy of Mongolia, a country rich in natural resources, is increasingly dependent upon the mining sector. International prices of mining commodities have been highly volatile in recent years. This paper uses a computable general equilibrium model to examine the short-term effects on the Mongolian economy of two scenarios: (1) a moderate boom in the coal market; and (2) a drop in the world price of metal ores. It is found that the Dutch disease effect generated by the shocks is insignificant given the structure of the economy (e.g., small export shares and low export intensity of manufacturing and agriculture commodities) and a labor market condition with high unemployment. Since the economy is largely dependent upon on the mining sector, the impacts of the shocks are jarring, implying that the government must abide by its fiscal rules for stable growth and prosperity.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.063
Threshold uncertainty score0.126

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.011
GPT teacher head0.149
Teacher spread0.138 · 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 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

Citations22
Published2018
Admission routes1
Has abstractyes

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