Dynamic regions and high-growth SMEs: uncertainty, potential information and weak signal networks
Bibliographic record
Abstract
The common elements of dynamic regional development can be summarized under three headings: the existence of absolute advantages such as plentiful mineral resources, large forest or significant tax benefits, etc.. Obviously derived from the absolute advantages: a significant reduction in economic uncertainty for investors. Together, these two elements explain the third, the massive inflow of foreign investments to the region. Many other dynamic regions do not have the same absolute advantages and their development is generated by hundreds of small local businesses and investments. We have therefore formulated a hypothesis to explain their dynamism in spite of their economic uncertainty and lack of absolute advantages. First, investors take advantage of different levels of complicity through networks that allow them to share and hence reduce uncertainty; and second, they increase their ability to innovate through the networks, which help them at least partially exceed their current innovative capacities. The networks – some of which are strong signal networks (usually regional) and others weak signal networks (regional or extra-regional) – promote the multiplication of fast growth SMEs which, in turn, stimulate the regional economy. We review the results of a case study (52 fast growth SMEs), highlighting the importance of potential information and weak signal networks in generating fast growth for SMEs.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".