How Can the Governance of the French Clusters (Pôles de Compétitivité) Improve SME’s Competitiveness?
Bibliographic record
Abstract
This paper focuses on Pôles de compétitivité—the French competitiveness clusters (FCC)—which mobilize national and regional actors and resources for innovation. By reviewing the literature (academic, web and news articles, and official reports) published on the subject, the synthesis emphasizes a collective learning process leading to institutional change reflected by legitimation of SMEs as full-fledged innovation actor. Through reflexive governance of certain poles, centered on their own sustainability, the policy has produced learning at local and national level. It has generated knowledge that has brought transformation of operational tools and societal representations in support of innovation of SMEs. The originality of the article is to show that in the French societal context, new place dependencies within the Pôles are characterized by emergence of a new innovation model of SMEs mainly through collaboration with public research. This model differs from the innovation model of SMEs staying outside of the poles. It is built through intermediary organizations that offer regional filters for national and regional policy adaptation. A major limitation of the policy is the difficulty to enhance cooperation between innovative SMEs and leader firms in the territory mainly because of a lack of social regulation over the protection and share of knowledge assets. The paper contributes to the research on clusters in general.
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How this classification was reachedexpand
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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".