22@ and the Innovation District in Barcelona and Montreal: A Process of Clustering Development between Urban Regeneration and Economic Competitiveness
Bibliographic record
Abstract
This paper analyzes the governance strategy of the 22@ District in Barcelona in order to assess the factors that explain its success and could support the economic reconversion of Montreal’s future Innovation District (ID), as well as that of other cities. We examine the case of the 22@ District as a former industrial neighbourhood seen as a “model” of urban regeneration and economic revitalization. Our assumption is that the world’s major cities are going through a phase based on the reorganization of central urban areas. Our article evaluates the main factors of urban regeneration in the 22@—district of innovation and it identifies elements of best practices in terms of governance which can be constructive for the “Quartier de l’Innovation” in Montreal and similar projects of other cities. The paper highlights the role of decision makers concerning the process of governance of 22@ and its historic changes, and insists on the the role of socioeconomic actors and territorial factors that could support the level of integration and implementation of Montreal@ID. Our paper highlights the importance of the integration process based on socio‐territorial innovations characterizing the Catalan context of 22@ as well as the Innovation District, something useful for other similar initiatives.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".