Transnational Entrepreneurs, Global Pipelines and Shifting Production Patterns: The Example of the Palanpuris in the Diamond Sector
Bibliographic record
Abstract
Building on the buzz-and-pipelines model of regional clusters, the paper shows that transnational entrepreneurs play an important role in the construction of external cluster relations and hence influence both the dynamics of regional clusters and global production settings. Unlike most studies on the economic implications of transnational migrants, the paper deals with a labor intensive manufacturing sector. In detail, diamond dealers from the Indian city of Palanpur will be conceptualized as transnational entrepreneurs who, in the past, were able to cover certain locations of the diamond value added chain with family members. The global relations set up by these families at the same time formed business networks allowing for an intense global exchange of knowledge and artifacts (diamonds). In the long run, these patterns implied a change of the overall production structures: in Antwerp, a traditional diamond trading and cutting center, the Indian dealers developed to strong competitors in the smaller stones segment and as such contributed to the fading away of the historically grown industrial base. In addition, the institutional support structures were partly dismantled. On the other hand, in India, a new cluster in diamond cutting emerged. The findings suggest that transnational entrepreneurs can contribute to a weakening of traditional cluster structures and therefore call for a more differentiated view as evoked by the one-sided focus of studies on returnee migrants in the high-tech sector.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".