Gated Adaptation during the Cape Town Drought: Mentalities, Transitions and Pathways to Partial Nodes of Water Security
Bibliographic record
Abstract
Illustrating how mentalities govern private responses to risk, this article highlights the importance of mental frames in the selection of adaptation pathways. Scholarship emanating out of the Cape Town drought (2015–2018) has drawn attention to the effect of the drought on public mentalities and their response to the drought, transitional governance arrangements and off-grid responses to secure water supply. This article focusses on what mentalities and behaviors may not have changed for private actors that secured water through off-grid means. This is a contrarian view to the dominant drought response discourse, yet critical for understanding and charting future governance arrangements. While it is acknowledged that transforming frames have emerged from the drought and are enabling novel pathways, the article questions the distributional and transition effect of such shifts when considering gated actions that link with conventional or untransformed views and behaviors which themselves entrench alternative response pathways for the affluent.HighlightsConventional frames govern private responses to risk.Mentalities drive the selection of available response technologies.Range of selected pathways indicate plural and differential views.Private off-grid and gated responses contest transformed views or behaviors.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.027 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".