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Record W7135603458

Curse or Blessing: natural resources and economic growth - comparison of the development of Botswana, Nigeria, Norway and Canada in early 21st century

2016· dissertation· cs· W7135603458 on OpenAlexaboutno aff
Adéla Zubíková

Bibliographic record

VenueDigital Repository (National Repository of Grey Literature) · 2016
Typedissertation
Languagecs
FieldEconomics, Econometrics and Finance
TopicNatural Resources and Economic Development
Canadian institutionsnot available
Fundersnot available
KeywordsCurseResource curseDutch diseasePoliticsNatural resourceCommodityCriticismGovernment (linguistics)
DOInot available

Abstract

fetched live from OpenAlex

This thesis seeks to verify the concept of so-called resource curse at the beginning of the new millennium. The theoretical part defines the symptoms of the alleged curse, curse transmission channels and criticism of the concept. Compared to other studies dealing with the theme of the resource curse this work is not focused on just one transmission channel. The practical part verifies several hypotheses established by comparing research papers on impacts of natural resources. The validity of the Prebisch-Singer hypothesis, Dutch disease symptoms and a negative impact on political institutions (inclination toward authoritarianism, high level of corruption, high government spending, low efficiency of economic and political decision-making and low investment in education) is verified. For the analysis have been selected two African countries (Nigeria and Botswana) and two advanced countries (Canada and Norway). The last part of this thesis provides policy implications. The results confirm the Prebisch-Singer hypothesis for selected commodities in the long term and some of the symptoms of Dutch disease at the beginning of the new millennium. Hypotheses regarding the impact on the political institutions have not been confirmed, since the results varied across the countries. The high vulnerability of the countries to movements in commodity prices was found.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.008
GPT teacher head0.209
Teacher spread0.201 · 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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