Describing and disentangling superdiversity through social networks
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
This paper is based on the analysis of 54 ego-centric network interviews conducted with migrants living in London and Toronto. With the backdrop that both of these cities can be considered as superdiverse socialising contexts the analysis aims to document how diversity can also be understood to be relational. To do this the paper first establishes the potential for diversity in the social networks and emphasises that this potential is embedded in changing trajectories of migration, labour market position and legal status. Subsequently, comparing attributes of respondents and their social contacts the paper shows that it is possible to measure homophily across a number of different superdiversity aspects. By visualising the resultant patterns of sameness, it does however become clear that those patterns are in fact very complex. In a final section the paper then tries to disentangle the visualised complexity using a fuzzy cmeans cluster analysis. Four socialising patterns are identified: city-cohort networks, peer group networks, long-term resident networks and superdiverse networks. The paper concludes by reflecting on how this analysis can contribute to shifting attention in researching the implications of international migration on urban social patterns towards appreciating and acknowledging patterns of complexity.
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.
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 | 0.002 | 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 | 0.007 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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