The roles of capitals in building capacity to address urban flooding in the shift to a new water management approach
Bibliographic record
Abstract
Stresses on water resources are considerable and will intensify in the future due to climatic and non-climatic drivers. The emerging shift from science-based command and control ‘old’ water management approach to a dynamic and integrative systems view of water—a ‘new’ water management approach—was explored using the concept of capacity, operationalized using the livelihoods capitals approach (i.e. physical, natural, financial, human and social capitals), as a conceptual lens in a multiple case study of notable cases of urban flooding from Canada and Australia. The findings show that there are changing conceptualizations of capacity in both cases over time. Physical and financial capitals have been emphasized for decades and are associated with the old water management approach, responding to major flood events with the construction of large control structures. While the importance of these capital inputs persists, the approach to building capacity under the emergence of the new water management approach places an increasing relative emphasis on social and human capitals. The lack of emphasis on natural capital persisted over time and should be considered explicitly in flood management. This study demonstrates how the capitals approach contributes to the very much needed understanding of how the shift from the old to a new water management approach is being expressed for both present-day decisions and long-term trajectories.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| 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".