Risk Narrative of Emergency and Disaster Management, Preparedness, and Planning (EDMPP): The Importance of the ‘Social’
Bibliographic record
Abstract
Risk perception, literacy, communication, narrative, governance, and education are important aspects of emergency and disaster management, preparedness, and planning (EDMPP) as they for example influence and direct EDMPP policies and actions. A thorough understanding of the ‘social aspects of risk is important for EDMPP, especially in relation to marginalized populations who are often overlooked. Technologies are increasingly employed for EDMPP. How these technology applications identify and engage with the ‘social’ of risk in general and the ‘social’ of risk experienced by marginalized populations is important for EDMPP. Equity, diversity, and inclusion (EDI) and similar phrases are employed as policy concepts to improve research, education, and participation in the workplace for marginalized groups such as women, Indigenous peoples, visible/racialized minorities, disabled people, and LGBTQ2S including in workplaces engaging with EDMPP which includes universities. The aim of this scoping review was to generate data that allows for a detailed understanding of the risk related discussions within the EDMPP academic literature as these discussions shape EDMPP policies and actions. The objective of this scoping review study was to map out the engagement with risk, specifically the social aspects of risk, in the EDMPP-focused academic literature with a focus on (a) EDMPP in general, (b) COVID-19, (c) EDMPP and marginalized groups, (d) EDMPP and patients, and (e) EDMPP and technologies (artificial intelligence, machine learning, machine reasoning, algorithm design approaches such as Bayesian belief networks, e-coaching, decision support systems, virtual coaching, automated decision support, e-mentoring, automated dialogue and conversational agents). Using the academic databases SCOPUS, Web of Sciences, and databases accessible under Compendex and EBSCO-HOST and performing hit count frequency searches of online and downloaded abstracts and thematic analysis of downloaded abstracts the study reveals a lack of coverage on the social aspects of risk and engagement with risk concepts such as risk perception, risk governance, risk literacy, risk communication, risk education and risk narrative especially in conjunction with marginalized groups and technologies employed in EDMPP decision support. Our findings suggest many opportunities to further the EDMPP academic inquiry by filling the gaps.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".