When Anthropocene shocks contest conventional mentalities: a case study from Cape Town
Bibliographic record
Abstract
Under conditions of protracted reduction in supply and heightened uncertainty, one of the notable responses to the Cape Town drought (2016–2018), was the proliferation of ‘water resilience’ in public and private discourses. Resilience was employed as an explanatory concept and governing tool, signalling a professed transition in the municipality’s understandings to an altered climate episteme – or what they have called, a ‘New Normal’. This article focuses on how public framings of resilience were used by the City of Cape Town to signal divorce from conventional approaches to climate and water. It contrasts conventional framings of a Holocene world, with those of a posited ‘mentality of the Anthropocene’ in order to elaborate this ostensible shift in mentality. Although this case study illustrates how public governors are finding utility in resilience as a term to facilitate explanation of their operating context, decisions and responses, contested and transitional mentalities elaborate why the municipality initially failed to anticipate, perceive and respond the drought. This article thereby highlights the cognitive tensions and practical challenges of transition for professionals patterned by conventional techno-managerial approaches, to a way of thinking more in line with reflexive and adaptive approaches anticipated to be necessary in an Anthropocene world.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.027 | 0.017 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".