Policy engagement as ‘empowered representation’: democratic mediation through a participatory research project on climate resilience
Bibliographic record
Abstract
Background: The article analyses the policy engagement component of a research project on climate resilience in vulnerable communities that took place in Cape Town, South Africa. Conducted in 2022, the engagement included community and stakeholder events in three research sites, and a cross-cutting policy event with municipal officials, held at the end of the project. Importantly, this policy engagement process occurred in a context of political marginalisation, that is, one characterised by low trust, and little meaningful representation or even communication between these vulnerable communities and the city. Aims and objectives: This article examines the impact of policy engagement on political relations between local government and vulnerable communities. Methods: The overall methodology of the article is qualitative, using an illustrative case-study research design to unpack the subjective experiences of both government officials and residents of vulnerable communities. Primary data included many primary documents, direct observation of the engagements and post-event interviews. Findings: First, the engagement process created new 'invented' spaces for the representation of community perspectives to the city, and the city's perspective to the community. Second, the engagement facilitated community self-representation through educating community members to advocate for their ideas in these new invented spaces. Third, this engagement tended to be more constructive and deliberative than polarising and confrontational. Discussion and conclusions: Drawing on the theoretical framework of 'political mediation', the policy engagement process is characterised as a positive instance of democratic mediation through 'empowered representation', with some specified limitations.
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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 | 0.093 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.046 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".