‘Partial functional redundancy’: An expression of household level resilience in response to climate risk
Bibliographic record
Abstract
This article extends ecological framings of resilience into socio-ecological and governance domains for urban infrastructure managers concerned with climate risk. Under moments of disruption, reliable and equitable access to adequate provision of public goods is anticipated to be increasingly challenging in cities across the world due to observed and anticipated disruptions of climate change and variability on city-wide infrastructures. Many cities facing such conditions are seeing rapid population and infrastructure growth enhancing exposure and vulnerability. One such example of disruptive climate risk is enhanced water scarcity. Private responses to the Cape Town drought adopted off-grid water technologies in order to secure their own supply while curtailing their dependence on the public water system. Unintended consequences of the nascent off-grid capacity created by private actors precipitated system transformations and accommodation challenges to disrupted public systems. The novel capacity generated through these responses to urban risk demonstrate what is identified here to be ‘partial functional redundancy’ – a key expression of resilience. Such response actions demonstrate partial and pragmatic expressions of redundancy through types of reserve capacity as a source of resilience in response to water insecurity.
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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 | 0.002 | 0.005 |
| 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 | 0.010 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".