Discourse, Power Dynamics, and Risk Amplification in Disaster Risk Management in Canada
Bibliographic record
Abstract
The domain of disaster risk management is rife with discursive contentions, whereby dominant discourses amplify the powers of risk actors to precipitate and reinforce political, economic, and environmental inequalities that predispose different sections of the population to unequal disaster risk vulnerabilities. This thesis identified important actors (government, risk experts, media, and NGOs) that shape the power dynamics in disaster risk management in Canada and explained their roles, influences, and the dimensions in which their powers negotiate each other through risk discourses. The patterns of these power dynamics in the three aspects of power –communication, assessment, and social trust –were also developed to provide a detailed description of how they form hegemonies that produce disaster inequality. The Power Amplified Risk Discourse (PARD) framework provides a theoretical framework for investigating the roles of discourses in creating and sustaining these power imbalances. PARD is an adaptation of the Social Amplification of Risk Framework (SARF) which can explain the complex cognitive, technical, and social dimensions to selective risk interpretations. Accordingly, PARD uses documentary and critical discourse analyses to investigate the roles of discourses in shaping the assessment and interpretation practices that reflect risk power imbalances. Analyses of the discursive and social practices also revealed that in many cases, these powers do not oppose each other, but rather work cooperatively to foist a risk hegemony as a means of self-perpetuation in risk management decision-making. The study also concludes that technical expertise, social trust, and privileged access to media constitute the biggest power factors for shaping risk discourse. Additionally, topic modeling and thematic analysis of social media data revealed the social impacts that could be directly attributed as the social consequences of these discursive power dynamics. The study suggests that the decentralized access to risk information and the growing distrust for institutional expertise significantly account for the social responses to power amplification in risk discourses. The study recommends a more inclusive approach to risk management and calls for restoration of trust between institutions and the public. Recommendations were also made for future research.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.033 | 0.015 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".