Ethnic entrepreneurial business cluster development: Chinatowns in Melbourne
Bibliographic record
Abstract
Purpose This paper aims to review the concept of clustering and to examine both mature and newly evolved natural ethnic entrepreneurial business clusters in Melbourne, Australia. Design/methodology/approach Phenomenological methodology was employed in this research. This qualitative research technique examines life experiences in an effort to understand and give them meaning. This method is seen to be appropriate as the study is investigative and explores the historical development, maintenance and growth of ethnic entrepreneurship clusters. Findings Box Hill has evolved into a second Chinatown in Melbourne through natural ethnic entrepreneurial business cluster. The key features of these entrepreneurs are high educational and professional competence; focus on hard work and persistence; independence and sense of freedom as the key driving force; maintaining cultural linkage with countries of origin; almost no assistance from government agencies; succession or exit is not a major issue; and strong belief in providing employment and making a contribution to society. Practical implications The ethnic Chinese entrepreneurs in Box Hill as well as in CBD Melbourne's Chinatown and the Chinese community at large realize that they needed to be socially participative and politically active. Through active participation in local politics, the ethnic community members are able to improve and provide more services and facilities to the community. As a result, the cluster becomes bigger and serves better the social needs of the community members, ethnic as well as non‐ethnic group members. Originality/value There is a paucity of literature on ethnic entrepreneurial business clusters that seem to be a growing feature of many cities such as Melbourne, Sydney, Vancouver, Los Angeles and other cities in the western hemisphere. This paper investigates this phenomenon in Melbourne.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".