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Record W3203803770 · doi:10.61801/ouaess.2021.1.08

Macroeconomic Impact of Natural Disasters in Albania

2021· article· en· W3203803770 on OpenAlexaff
Valbona Çinaj, Rebeka Ribaj

Bibliographic record

VenueOvidius University Annals Economic Sciences Series · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicInsurance and Financial Risk Management
Canadian institutionsTed Rogers Centre for Heart Research
Fundersnot available
KeywordsDamagesNatural disasterNatural (archaeology)Economic impact analysisEconomyEconomicsBusinessNatural resource economicsGeographyPolitical science

Abstract

fetched live from OpenAlex

This paper has aim to take a general look at the impact that natural disasters have on a country's economy. Further expanding concretely in the impact caused by the earthquake of November 26, 2019, in the economy of Albania. This paper studied macroeconomic indicators over the past few years, forecasts for their progress before the natural disaster, and how they are projected to change after that. Damages and losses will be analyzed according to specific sectors of the economy as well as the impact on the main affected municipalities. The paper presents the authors conclusions and recommendations regarding the actions to be taken to help with the least harmful way of the earthquake.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.503
Threshold uncertainty score0.750

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.034
GPT teacher head0.245
Teacher spread0.211 · 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.

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

Citations3
Published2021
Admission routes1
Has abstractyes

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