Las economías de localización en el sector cultural: una propuesta sobre factores conformadores de capitalidad cultural aplicado al caso de Barcelona
Bibliographic record
Abstract
El objeto del presente artículo es confrontar críticamente el debate teórico sobre distritos industriales y economías de localización aplicados a la producción y a la demanda cultural, en un momento de transformación post-industrial de los modelos de desarrollo económico. Se pretende, asimismo, presentar los factores motores que caracterizan una gran capital cultural contemporánea y los distintos condicionantes de base necesarios que la definen. También se reflexiona sobre la interdependencia de dichos factores y su papel catalizador, para terminar enfatizando sobre la capacidad de detonante que pueden tener determinadas políticas gubernamentales activas de tipo económico, cultural o urbano. Algunos de dichos factores y políticas se analizan a la luz del caso barcelonés y de su proceso de consolidación a lo largo de los últimos quince años como una de las grandes capitales culturales del sur de Europa.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; 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".