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Record W4416084758 · doi:10.1017/9781009633574

Legacy in the Landscape

2025· book· W4416084758 on OpenAlexaff
Stephanie E. Chang

Bibliographic record

VenueCambridge University Press eBooks · 2025
Typebook
Language
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsContext (archaeology)Perspective (graphical)Natural disasterNatural hazardUrban planningSocioeconomic status

Abstract

fetched live from OpenAlex

With its focus on the city rather than the disaster event, this book situates natural disasters in the context of urban growth and change. It offers an original, interdisciplinary perspective by connecting the technical and socioeconomic dimensions of disaster risk and highlighting the commonalities of hazards such as river flooding, coastal flooding, and earthquakes. The book begins by proposing a novel Urban Risk Dynamics framework that emphasizes the roles of economy, landscape, and technology in influencing hazard, exposure, and vulnerability. This framework is then used to support the examination of six contrasting cities from around the world, offering generalized insights that apply to a wide range of urban risk contexts. The book will be of significant interest to students and researchers working in urban planning, civil engineering, Earth sciences, and environmental science, and to policy makers and practitioners concerned with reducing future disaster risk in cities.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.007
Scholarly communication0.0090.005
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0230.005

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 head0.202
Teacher spread0.190 · 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 designNot applicable
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
Published2025
Admission routes1
Has abstractyes

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