Innovative firms behind the regions: Analysis of regional innovation performance in Portugal by external logistic biplots
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The strategic choices regarding innovation and research and development (R&D) policy in Portugal have, over the past two decades, produced various positive benefits, in which the regions of Lisbon and Algarve, in particular, have taken the lead. These are the only regions in Portugal which converge towards the European average growth rate with respect to gross production, investment and employment creation. It is now timely to evaluate firms’ contributions to national and regional growth, their obstacles, and impacts. After a conceptualization of innovation policy in Portugal, the present paper treats innovation as a major criterion for the policy evaluation process referred to above. Our empirical investigation aims to explain the innovation performance of Portuguese firms throughout the country, and to explore those determinants of innovation which are region-specific. Therefore, the analysis addresses a set of firms’ achievement patterns, by focusing on ways in which institutions interact in the process of innovation at the regional level. In our modelling study, we employ a new methodology, viz. the external logistic biplot method, which is applied to an extensive sample of innovative institutions in Portugal. Variables identified as crucial determinants in earlier studies are used to describe regional institutional profiles. Such profiles exhibit a great variety of ways in which these determinants are able to promote regional innovation. The creation of a Gradient of Capacity to Dynamically Innovate associated with each firm enables an analysis of the innovation gradient of each region in Portugal. Our paper presents and investigates these findings, and offers some policy lessons.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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 it