Cooperation, Proximity, and Social Innovation: Three Ingredients for Industrial Medium-Sized Towns’ Renewal?
Bibliographic record
Abstract
Over several decades, medium-sized industrial towns have suffered from a combination of economic and political processes: Deindustrialization, metropolization, and withdrawal of public services. After two decades in which they have been somewhat neglected (in favor of metropolises), there have recently been State and European public policies aimed at them. Medium-sized cities are not homogeneous and present several trajectories. Based on quantitative approach in France, we highlight the very diverse socio-economic dynamics of French medium-sized industrial towns. Thus, far from widespread decline or shrinking dynamics, some of these cities are experiencing an economic rebound. This is the case of Romans-sur-Isère, a medium-sized town located in the south-east of France. Focusing our qualitative analyze on this city, we try to understand this type of process. In this medium-sized town, former capital of the shoe industry, local stakeholders, private, and public try to support a productive renewal. The results of our case study highlight the role that cooperation, spatial and organizational proximity, and social innovation could play in the renewal of productive economy in medium-sized industrial towns. Even if the economic situation remains difficult for many medium-sized cities in France as in Europe, we argue that they could have a productive future making and ultimately take advantages of their “medium-sized” attributes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".