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Record W2076199410 · doi:10.3828/tpr.2011.35

Viewpoint: Reconstruction after natural disasters: the opportunities and constraints facing our cities

2011· article· en· W2076199410 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTown Planning Review · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNatural disasterGovernment (linguistics)Port (circuit theory)Economic growthGeographyEnvironmental planningPolitical scienceEngineeringMeteorology

Abstract

fetched live from OpenAlex

Each year many parts of the world are inflicted with one type of disaster or another. In the six months or so prior to the writing of this Viewpoint, media reports covered: the impact of a hurricane in Cairns, northern Queensland, Australia; three devastating earthquakes in Christchurch, New Zealand; and a triple disaster in Japan involving an earthquake followed by a tsunami, followed by a nuclear power plant emergency. The past few years have seen further catastrophic earthquakes that devastated the capital of Haiti (Port-au-Prince) and Kobe, Japan, as well as Hurricane Katrina, which ravaged New Orleans and the southern Gulf states of the USA. The contexts of these and other major events vary widely - from the poor countries of the developing world to the world's richest countries - yet the loss of lives, infrastructure and property damage in each case has been massive. Planning for reconstruction What becomes of these communities and regions afterwards? How long does it take to rebuild? Is there anything planning professionals can do to speed the process, to reduce the losses, to help communities become more resilient? Regardless of the severity of disaster, or the level of assistance from the outside world or higher levels of government, the primary responsibility for recovery ultimately falls on local governments. Little information exists, however, to guide local decision-makers and local planners. The American Planning Association (APA) has recently launched a threeyear research project to examine how planning for reconstructing cities after major disasters might provide ready-to-use information for practitioners (APA, 2010). The World Bank has already compiled a training manual of best practices in post-disaster housing and community reconstruction, aimed at international project managers charged with implementing reconstruction aid (Jha and Duyne, 2010). Recovery of cities after major catastrophes cannot be examined in isolation. The so-called 'Disaster Life Cycle' model developed to help understand how to respond to natural and technological hazards comprises four phases: pre-disaster preparedness; emergency responses (including search and rescue); recovery and reconstruction; and finally mitigation against further known hazards (Mileti, 1999). Many cities now prepare plans that focus on emergency response and preparedness issues, traditionally the ambit of civil defense or municipal emergency officers. Planners, however, give little attention to the likelihood of a major disaster, and even less is given by all professionals to longer-term recovery and reconstruction issues (Meyer et al., 2010; Sandink and Fuller, 2009). This is not to suggest that planning for immediate emergency response and short-term repairs to housing and infrastructure following floods, earthquakes and other disasters are unimportant - they are. But in the case of catastrophic disasters the longer-term rebuilding of cities and regions brings an entirely different set of problems. The desired outcome is for communities to emerge from post-disaster recovery and long-term reconstruction safer and less vulnerable to future calamities. A range of mitigation measures can be usefully incorporated during the recovery phase, including improved building codes and construction standards, as well as more effective land-use regulations and community-planning arrangements. Above all else, planners should use the post-disaster period as a window of opportunity to incorporate sustainable development principles and apply holistic management approaches to rebuilding cities. Research into reconstruction planning The term 'recovery' has been used interchangeably with 'reconstruction', 'restoration' and 'rehabilitation', as well as post-disaster 'redevelopment'. Over the years, researchers studying community recovery have recognised that it is difficult to generalise this component of planning for disasters because of the great variation in localities impacted, as well as the disaster events themselves. …

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.893
Threshold uncertainty score0.438

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.101
GPT teacher head0.314
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