Cross-local knowledge fertilization, cluster emergence, and the generation of buzz
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recent work in economic geography has investigated how clusters evolve and change over time. Yet our understanding of such processes is still incomplete. Many accounts rest on a perspective that focuses on the development of solitary knowledge ecologies, while neglecting the fact that cluster formation may be triggered when different places are connected and begin to influence each other in mutual beneficial ways. This article argues that conceptualizations of cluster emergence need to understand the crucial ways in which this process is from the very beginning associated with external linkages and trans-local pipelines. A model of cluster formation is presented that suggests how buzz generation is driven by the connections between different localities in four stages: (i) pioneering, (ii) expansion, (iii) off-shoot, and (iv) fusion. We use the case of the global diamond industry, in both inductive and deductive ways, as an example to show that transnational communities and tight networks play a crucial role in forming cross-local connections that can trigger cluster emergence.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it