Socialising with diversity: numerical smallness, social networks and urban superdiversity
Bibliographic record
Abstract
The notion of superdiversity demands a move beyond an ethno-focal analysis of migration related diversity and calls to analytically incorporate other aspects of diversification, including differential migration, legal status and labour market trajectories. Taking London and Toronto as field locations, this thesis investigates how a superdiversity lens can be operationalised and utilised to discuss migrant socialities in urban contexts. It methodologically explores one particular avenue for doing this - personal social network analysis - to better understand the theoretical and empirical implications of adopting a superdiversity approach. Both qualitative and quantitative analysis strategies are used and particular emphasis is on visualising complex patterns and exploring how starting with complexity as an assumption facilitates the multidimensional analysis a superdiversity lens calls for. Focusing on networks of migrants who in statistical terms are commonly categorised as 'other' - who have relatively few co-migrants in terms of place of origin but who are differentiated in terms of other superdiversity aspects - the thesis questions if and what impact small group size has on patterns of sociality. With this focus it is established that a) the numerical size of the origin group impacts on social activities differently depending on whether one small group is explicitly liked to other pan-ethnic groups or not; b) that sociality patterns of migrants emerge from the complex interplay of general socialising opportunities but are also linked to individual trajectories of migration and settlement; c) that with a superdiversity lens it is indeed possible to move beyond the ethnic network notion. To support this latter point the thesis explores four alternative ways of describing migrant networks in terms of city-cohort, long-term resident, superdiverse and migrant-peer networks. The analysis contributes to theoretical debates by proposing a rational understanding of diversity rather than one based on the enumeration of categories be they ethnic or otherwise.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".