Visualizing superdiversity and “seeing” urban socio-economic complexity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recent migration has made traditional destination cities so diverse that many conventional social science concepts and methods have become inadequate to the task of understanding complex diversity, or what is now often termed superdiversity. Here, we address the need for new methods of "seeing" urban superdiversity in two ways. First, we highlight the need to understand urban contexts by examining new combinations and intersections of multiple social variables. Second, we demonstrate a suite of new interactive tools. We attempt to enable users to picture, perceive and apprehend complex analyses of multidimensional data on urban diversity in new, more intuitive ways. This visualization draws on multivariate geo-spatial data on different kinds of diversity, across three major destination cities: Sydney, Vancouver, and Auckland. We believe this approach contributes to the theoretical and methodological refinements needed to study contemporary superdiversity in urban settings, and to contribute to better public understanding and policies regarding the processes of urban diversification.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it