How Europe's Economies Learn:Coordinating Competing Models
Bibliographic record
Abstract
When seeking to bench mark the performance of European economies, commentators often look to compare them to the economies of Japan and the United States. How Europe's Economies Learn shows how this is seriously misleading, and how any such comparison needs to be complemented with an understanding of the fundamental differences between Europe's economies. The contributors provide an up-to-date description and analysis of the way differences in state systems and institutional contexts, such as labour markets, education and training systems, and financial systems, shape learning processes and innovation performance across the member nations of the European Union. In doing so, it draws important conclusion for how policy strategies should be designed at the national and European levels in order to further promote the goals of the Lisbon process. Contributors to this volume - Christian Bessy, CNRS Research Fellow, Ecole Normale Superieure de Cachan, France, Adne Cappelen, Director of Research, Statistics Norway, Norway, Patrick Cohendet, Professor, Department of International Business, HEC School of Management, Canada, Giovanni Dosi, Professor, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Italy, Mei Ho, Doctoral Candidate, Eindhoven Centre for Innovation Studies, Eindhoven Technical University, The Netherlands, Mauro Sylos Labini, Doctoral Candidate, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Italy, Alice Lam, Professor of Organization Studies, School of Management, Royal Holloway University of London, UK, Caroline Lanciano-Morandat, CNRS Research Fellow, Laboratory of Economics and Labour Sociology (LEST), University of Aix-en-Provence, France, Patrick Llerena, Professor, Office for Theoretical and Applied Economics (BETA) University Louis Pasteur, France, Edward Lorenz, Professor, University of Nice-Sophia Antipolis, France, Bengt-Ake Lundvall, Professor, Department of Business Studies, Aalborg University, Denmark, Chantale Mailhot, Associate Professor, Department of International Business, HEC School of Management, Canada, Peter Nielsen, Professor, Aalborg University, Denmark, Hiroatsu Nohara, CNRS Research Fellow, Laboratory of Economics and Labour Sociology (LEST), University of Aix-en-Provence, France, Maria Joao Rodrigues, President, European Commission's Advisory Group for Social Sciences and Professor, University Institute, Portugal, Veronique Schaeffer, Assistant Professor, Office for Theoretical and Applied Economics (BETA) University Louis Pasteur, France, Mark Tomlinson, Lecturer, Birmingham Business School, University of Birmingham, UK, Andrew Tylecote, Professor, Management School, University of Sheffield, UK, Antoine Valeyre, CNRS Research Fellow, Centre for Employment Studies (CEE), France, Eric Verdier, CNRS Research Director, Laboratory of Economics and Labour Sociology (LEST) University of Aix-en-Provence, France, Bart Verspagen, Professor, Department of Technology Management, Eindhoven Technical University, The Netherlands, Richard Whitley, Professor of Organizational Sociology and Director of Research, Manchester Business School, University of Manchester, UK.
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.025 | 0.028 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".