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Record W3012463684 · doi:10.30658/jicrcr.3.1.2

Mortality Reporting and Rumor Generation: An Assessment of Crisis and Emergency Risk Communication following Hurricane María in Puerto Rico

2020· article· en· W3012463684 on OpenAlexfundno aff
Elizabeth Andrade, Nicole Barrett, Mark Edberg, María I. Rivera, Ljubica Latinovic, Matthew W. Seeger, Ann Goldman-Hawes, Carlos Santos‐Burgoa

Bibliographic record

VenueJournal of International Crisis and Risk Communication Research · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Relations and Crisis Communication
Canadian institutionsnot available
FundersCenters for Disease Control and PreventionCanada Excellence Research Chairs, Government of Canada
KeywordsCredibilityGovernment (linguistics)RumorCrisis communicationPreparednessSocial mediaPublic relationsTransparency (behavior)Political scienceEmergency managementBusiness

Abstract

fetched live from OpenAlex

This study assessed the Government of Puerto Rico’s crisis and emergency risk communications following Hurricane María and the post-disaster information environment to identify factors that may have contributed to negative public perceptions of mortality reports. Data included Government of Puerto Rico press releases, press conference audio recordings and Facebook Live transmissions, digital media news and social media commentary, and interviews with Government of Puerto Rico personnel and community stakeholders. Study findings indicate that inadequate crisis communication planning and training, coupled with information gaps and inconsistencies, contributed to rumors around the issue of mortality. As a consequence, the Government of Puerto Rico lost the ability to effectively manage messaging, thus decreasing their credibility, perceived transparency, and public trust. Recommendations regarding future preparedness activities and research are offered.

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)0.015
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.126
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.177
GPT teacher head0.520
Teacher spread0.344 · 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

Citations32
Published2020
Admission routes1
Has abstractyes

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