Iniciativa emprenderora e innovación en Barcelona y Montreal. Une comparación del grado de completitud de las políticas
Bibliographic record
Abstract
Las políticas de entrepreneuship e innovación han ido convergiendo en los últimos años en el marco de las políticas de apoyo a la competitividad. El presente trabajo utiliza la metodología de la red IPREG para analizar el grado de “completitud”, y las principales características de estas dos políticas a nivel local. Se desarrolla el caso de las ciudades de Barcelona y Montreal, dos ciudades con numerosas características similares, identificadas ambas como polos de “creatividad e innovación”. En políticas de apoyo a la iniciativa emprendedora los resultados evidencian un mayor grado de completitud en Montreal mientras que en innovación se da el caso contrario. Las diferencias más significativas entre las políticas de las dos ciudades se evidencian en tres áreas: el apoyo a grupos objetivo, la educación y la eliminación de barreras.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".