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Record W2124649692 · doi:10.1017/s0022050709000850

The Opportunity of a Disaster: The Economic Impact of the 1755 Lisbon Earthquake

2009· article· en· W2124649692 on OpenAlexaff
Álvaro Santos Pereira

Bibliographic record

VenueThe Journal of Economic History · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsTragedy (event)PortugueseNatural disasterFriendshipWageOrder (exchange)EconomicsEconomyPolitical scienceGeographySociologyLabour economicsFinanceSocial science

Abstract

fetched live from OpenAlex

By combining new archival and existing data, this article provides estimates of the economic impact of the 1755 Lisbon earthquake, the largest natural catastrophe ever recorded in Europe. The direct cost of the earthquake is estimated to be between 32 and 48 percent of the Portuguese GDP. In spite of strict controls, prices and wages remained volatile in the years after the tragedy. The recovery from the earthquake also led to a rise in the wage premium of construction workers. More significantly, the earthquake became an opportunity to reform the economy and to reduce the economic semi-dependency vis-à-vis Britain. “ Sometimes miracles are necessary, natural phenomena, or great disasters in order to shake, to awaken, and to open the eyes of misled nations about their interests, [nations] oppressed by others that simulate friendship, and reciprocal interest. Portugal needed the earthquake to open her eyes, and to little by little escape from slavery and total ruin.”1

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)0.000
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 score0.077
Threshold uncertainty score0.154

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.006
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.026
GPT teacher head0.226
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 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

Citations96
Published2009
Admission routes1
Has abstractyes

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