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Record W3177618397 · doi:10.1145/3463928

A Patent Application for NEXTGEN Flood Early Warning System

2021· article· en· W3177618397 on OpenAlexaff
Robert Soden, Nicolas LaLone, Dharma Dailey

Bibliographic record

VenueProceedings of the ACM on Human-Computer Interaction · 2021
Typearticle
Languageen
FieldComputer Science
TopicInformation and Cyber Security
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsWarning systemFlood mythWork (physics)InformaticsOrder (exchange)Disaster responseEmergency managementBusinessEngineeringComputer scienceComputer securityPolitical scienceTelecommunicationsGeography

Abstract

fetched live from OpenAlex

This design fiction re-imagines an important informational element of the flood early warning system in order to unpack some of the questionable assumptions that society makes about disaster. In presenting an updated, ironic, vision of an alternative system, we highlight some of the ways that received ideas about the root causes of disaster, who is responsible for public safety, and the role of private sector innovation, are so embedded in the design of technologies used in crisis management that they have become taken for granted. This work demonstrates the potential for design fiction to serve as a tool in the evaluation and critique of safety-critical information systems and as a communication tool for conveying the complex findings of disaster research. It also points to new avenues of exploration for crisis informatics work on public warning systems.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.779
Threshold uncertainty score0.462

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 science0.0010.001
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 head0.052
GPT teacher head0.285
Teacher spread0.233 · 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 designTheoretical or conceptual
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

Citations2
Published2021
Admission routes1
Has abstractyes

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