Bibliographic record
Abstract
In recent urban studies literature, it has been recognised that ethnic settlements in cities have undergone significant transformations, largely as a result of the 'globalisation' process. The term ethnoburb, for example, has begun to be used recently in reference to new suburban Chinese settlements in North American cities (particularly Los Angeles). These settlements have proved to be quantitatively different from traditional 'Chinatowns' in a number of ways. While accepting this new model of the Chinese ethnoburb (Li 1998), this paper goes on to ask how these changes, resulting largely from globalisation, and the rise of transnationalism and cosmopolitanism, impact on the experience of this new space of immigration. That is, how is living and being in an ethnoburb different from living in a Chinatown? Through the use of in-depth interview data of Chinese-Canadian residents and users of the Richmond, British Columbia Chinese ethnoburb, I argue in this paper that the fundamental experiential characteristic of the Chinese ethnoburb is one of mobility (Urry 2000), which results in a fundamentally different ethnic social space, characterised by the experience of movement and the ability to be 'elsewhere'. In this sense, Richmond can be seen as a 'space of flows' rather that an 'ethnic enclave'. This is illustrated through and an examination of the mobilities of bodies, objects, and imaginations within the 'space' of the Richmond ethnoburb.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.032 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| 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".