An Anatomy of Bengaluru's ICT Cluster: A Community Detection Approach
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT We use community detection analysis to investigate the structure of Bengaluru's ICT cluster's inter-organizational network during the period 2015–2017. Building on the knowledge sourcing literature, we conjecture that cluster firms primarily build knowledge-seeking horizontal linkages with technologically similar companies, and that this splits the network into multiple technological communities within which firms are tightly connected, but between which linkages are scarce. We further propose that community-spanning firms which build horizontal linkages that bridge technological communities are more likely to conduct radical innovation than their peers. We finally argue that no relation exists between technological proximity and community formation in the network of vertical buyer-supplier relations. Using a voltage-based algorithm for community discovery, we draw empirical support for these predictions. We discuss the implications of our findings for Bengaluru's upgrading potential.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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