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Record W2020861529 · doi:10.5430/jms.v5n3p16

Instructional Crisis Communication: Connecting Ethnicity and Sex in the Assessment of Receiver-Oriented Message Effectiveness

2014· article· en· W2020861529 on OpenAlexvenueno aff
Robert S. Littlefield, Kimberly Beauchamp, Derek R. Lane, Deanna D. Sellnow, Timothy L. Sellnow, Steven Venette, Bethany Wilson

Bibliographic record

VenueJournal of Management and Strategy · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Relations and Crisis Communication
Canadian institutionsnot available
Fundersnot available
KeywordsEthnic groupPerceptionPsychologyAction (physics)Social psychologyPolitical science

Abstract

fetched live from OpenAlex

This study explored the responses of receivers of risk messages that included all elements of the Internalization-Distribution-Explanation-Action (IDEA) learning cycle model to determine if culture, sex, and socio-economic status had any impact on receptivity and behavioral intention. Using a pre- and post-measures experimental design, 746 participants from different geographic areas within the United States watched prepared news clips. Participants identified their learning styles, the perceived message effectiveness, and their behavioral intentions following their observation. Results suggest that messages addressing all elements of the IDEA model were perceived as more effective by participants. Ethnicity and sex of participants in some cases made a difference regarding perception of message effectiveness.

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)0.007
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.027
GPT teacher head0.345
Teacher spread0.319 · 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 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

Citations15
Published2014
Admission routes1
Has abstractyes

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