“Currying favour with the locals”: Balti owners and business enclaves
Bibliographic record
Abstract
The often‐dynamic presence of South Asians in particular economic activities has prompted ambivalent responses from policymakers. For some, there is encouragement to “break out” from ethnic niche businesses like lower‐order retailing and catering. Another ploy is to promote a strategy of “‘ethnic advantage” by exploiting “cultural” features of a particular community. Examples include the marketing of what can be termed “ethnic enclaves” like “Chinatown” in Manchester and “Little Italy” in Boston (USA). This paper reports on an initiative to exploit the tourist potential of South Asian cuisine by developing a “Balti Quarter” in Birmingham. The results highlight a number of key issues involved in operationalising this increasingly popular strategy. First, the unitarist conceptualisation of the notion of an ethnic enclave obscures the harshly competitive environment that small ethnic minority firms like those in the “Balti Quarter” have to operate in. Second, the often ad hoc way in which such inner city areas are regulated (through planning guidelines) can intensify the competitive pressures facing many firms in the area. Finally, the “external” focus of the initiative runs the risk of masking chronic issues within the firm (e.g. poor working environments) which policymakers should be equally concerned with.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".