Exploring high-tech specializations with the use of metadata: evidence from the metropolitan clusters of Paris and Toronto
Bibliographic record
Abstract
High-tech companies, sectors and hubs are recognized as important drivers of economic growth, innovation and productivity; however, the conventional administrative datasets and industry codes have proved to be often inadequate when it comes to properly detect and analyse new technological domains. In this paper, we propose a simple approach and suggest a promising data source that can be used to identify the most relevant high-tech specializations existing in a certain region, such as an innovative metropolitan cluster; importantly, it highlights the emerging complementarities between technological domains, which can represent the starting point for cooperation and synergies between companies presenting different core activities but also some degree of common knowledge. Hence, this unconventional mapping of a technological landscape can provide useful information to companies, and also to policymakers who intend to support high-tech businesses and sectors. To implement the proposed approach, we use first-hand information on firms’ main products, markets and technologies and implement the tool of network analysis, which we apply to two particularly promising technological hubs, namely, the metropolitan clusters of Paris and Toronto.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".