The architecture of firms’ innovative behaviors
Bibliographic record
Abstract
During the last decades the amount of studies published about innovation systems has been massive, originating a great interest for policy makers in search for scientific background and technical support to find out the most adequate strategies for development. Although from different perspectives, studies point out knowledge creation and innovation, as the major drivers of change and growth. The consensus is broken, however, as soon as the complexity of innovation and knowledge are tackled: Innovation goes much beyond new product or process development due to its interactive nature, and knowledge surpasses the firms’ attributes because, frequently, it is a spatial endogenous characteristic. The present paper is a contribution to the earlier discussion and represents an effort to develop a model able to answer how institutions are relating to each other, tracing networks of innovation. The available database compromises an extensive set of Portuguese innovative firms, spatially identified and able to permit spatial connectivity to understand where and how strong are the links for innovation in Portugal and to analyze the respective level of concentration or dispersion. Ryerson University, Department of Geography, Toronto, Canada CIEO – Research Centre for Spatial and Organizational Dynamics, Faro, Portugal VU University Amsterdam, Faculty of Economics and Business Administration, Amsterdam, the Netherlands
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".