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Record W2109211801 · doi:10.24908/pceea.v0i0.3723

EARTHQUAKE ENGINEERING EDUCATION: THE MEXICO-JAPAN EXPERIENCE

2011· article· en· W2109211801 on OpenAlexvenueno aff
Chávez Galindo

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsnot available
Fundersnot available
KeywordsWork (physics)Agency (philosophy)LandslidePolitical scienceEmergency managementTollPopulationEconomic growthGeographyEnvironmental planningEngineeringSociologySocial scienceMedicine

Abstract

fetched live from OpenAlex

More than twenty years ago, Mexico City suffered the most disastrous earthquake in its history. The death toll from the 1985 Earthquake was an estimated 10,000, with a further 30,000 injured and 100,000 left homeless. 416 buildings were destroyed and over 3,000 seriously damaged. After this event, the profound necessity of effective strategies lead to the creation of the Earthquake Disaster Prevention Project, supported by the Japan International Cooperation Agency (JICA). The main goal of this project was to contribute to the safety of the Mexican population, and eventually become an important aid also for Central America and the Caribbean region. In 1996, the Earthquake Disaster Prevention Project between Japan and Mexico was successfully completed. The most significant symbol of its achievements is the research facility called CENAPRED. The main purpose of this center has been to work as an organization executing investigations, training and disseminating activities. From that time, various research activities regarding the disaster prevention have been carried out, establishing a new follow-up era of cooperation. Nowadays, the cooperation between Japan and Mexico keeps strengthening and now includes many areas of action. It carries out, promotes and coordinates research activities not only in terms of earthquake protection strategies, but in general for any type of disaster, like volcanoes or landslides. A successful example is the MEXT scholarship program, which provides high level education to students from developing countries. Every year hundreds of foreign students arrive to Japan to carry out graduate studies in different areas, being engineering one of the strongest.

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)0.001
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 score0.443
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.012
GPT teacher head0.226
Teacher spread0.214 · 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

Citations0
Published2011
Admission routes1
Has abstractyes

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