An assessment of partnerships in flood emergency management, Red River Valley, Manitoba
Bibliographic record
Abstract
Floods are one of many natural and human-induced hazards that threaten Manitoba annually. Emergency management tasks are assumed by a variety of organizations to protect people and resources from catastrophic loss and death by undertaking preparedness, response, recovery and mitigation actions and activities. As the climate changes at an unprecedented pace, flooding and other hazards become more uncertain. In an effort to address these uncertain flood (and other) hazards facing Manitoba, a number of partnerships have formed among government, private, non-government, and community-based organizations to better address hazard and disaster issues in the province. Combining two research methods, namely, a modified Delphi technique and a multiple case study of five specific partnerships, this thesis assesses the use of institutional partnerships in a flood emergency management context in the Red River Valley, Manitoba. The objectives of this study aimed to identify and examine the types of institutional partnerships that exist, to assess partnerships using characteristics of success as performance indicators, to determine the role of interpersonal relationships and networking in successful partnerships and to provide recommendations for partnerships in emergency management. The analysis provided detailed assessments of five partnerships. The characteristics of successful partnerships indicated that four of the five partnerships assessed will likely be successful in the event of a future disaster. Partnerships with strong interpersonal relationships and networking among partners and related organizations are critical to the development and maintenance of successful partnerships. Overall, it is recommended that partnerships in emergency management continue to be cultivated and to expand partner members and linkages beyond the scope of emergency management institutions.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".