Scaling discourse analysis: Experiences from Hermanus, South Africa and Walvis Bay, Namibia1
Bibliographic record
Abstract
Scaling discourse analysis refers to the necessity to consider environmental discourse a multi-dimensional and diversified practice. Depending on the various levels of state and society at which environmental policies are applied and depending on the geographical scale at which their solution is sought, we have to differentiate both policy processes and outcomes in environmental politics. We introduce the importance of scale in mapping the multiple trajectories through which complex and intertwined relations of power produce and reproduce uneven geographies in the area of urban environmental policy. More specifically, we are seeking to cast light on the relationships between scale, discourse and the politics of urban environments. Using an approach influenced by urban political ecology, the relevant discourses here are constructed in a triangle of terms: urban, ecology and policy. In this triangle, there are no givens and invariables. Its three points are constituted through contested discourses and practices. We approach our analysis from an understanding of urban water policies in two municipalities Namibia and South Africa as the outcome of a discursive and material practice operating at various levels of state and society and as an integral part of wider processes of social and political change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.033 | 0.016 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 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".