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Old Media, New Media, and the Complex Story of Disasters

2016· reference-entry· en· W2508985924 on OpenAlexaff
Josh Greenberg, Thomas Scanlon

Bibliographic record

VenueOxford Research Encyclopedia of Natural Hazard Science · 2016
Typereference-entry
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsCarleton University
Fundersnot available
KeywordsNatural disasterSocial mediaTerrorismNothingEmergency managementNews mediaMedia studiesPolitical sciencePublic relationsGeographyEngineeringSociologyMeteorology

Abstract

fetched live from OpenAlex

Abstract Media have always played an important role in times of emergency and disaster. Undersea cables, international news agencies, the press, radio and television, and, most recently, digital and mobile technologies—all have played myriad and complex roles in supporting emergency response and notification, and in helping constitute a shared experience that can be important to social mobilization and community formation. The geographical location of disasters and the identities of victims, the increasingly visual nature of disaster events, and the ubiquitous nature of media in our lives, all shape and influence which kinds of emergencies attract global media and public attention, and how we come to understand them. Globalization has compressed time and space such that a whole range of disasters—from natural events (cyclones, earthquakes, and hurricanes) to industrial accidents and terrorist attacks—appear on our television and mobile screens with almost daily frequency. There is nothing inherent about these events that give them meaning—they occur in a real, material world; but for many of us, our experience of these events is shaped and determined in large part by our interactions with media industries, institutions, and technologies. Understanding the media’s construction of these events as disasters provides important insight into the nature of disaster mitigation, response and recovery.

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)0.010
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.791
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0020.032
Scholarly communication0.0000.001
Open science0.0050.002
Research integrity0.0000.002
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 head0.061
GPT teacher head0.359
Teacher spread0.298 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations20
Published2016
Admission routes1
Has abstractyes

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