Information Seeking and Sharing as Gratifications Explaining Mobile Social Media Use in Pre-, During and Post-Disaster Management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper suggests a model and empirical study to explore the acceptance of mobile social media and other applications for disaster risk mitigation in sub-Saharan Africa, using gratification and technology acceptance theories. It identifies information seeking and sharing as gratifications leading to increased intention and actual usage of mobile platforms for disaster management. Overall, our model suggests that social media and app usage positively impact disaster mitigation, preparedness, awareness, response, coping, and adaptation. Emphasizing gratification theory, particularly in marginalized African communities, and confirming the relevance of technology acceptance theories, our study promises to contribute to IT-enabled disaster management literature. Data will be collected through a survey from different regions across Sub-Saharan Africa, with subsequent analysis using partial least squares structural equation modelling (PLS-SEM).
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it