Innovation and Firm Performance. Econometric Explorations of Survey Data
Bibliographic record
Abstract
Introduction A.Kleinknecht & P.Mohnen PART ONE: COMPARING INNOVATION INDICATORS Towards an Innovation Intensity Index: the Case of CIS-I in Denmark and Ireland P.Mohnen & M.Dagenais Innovations, Patents and Cash Flow P.Geroski, J.Van Reenen & C.Walters The Mutual Relation Between Patents and R H.van Ophem, E.Brouwe, A.Kleinknecht & P.Mohnen PART TWO: DETERMINANTS OF INNOVATIVE BEHAVIOUR Innovation and Farm Performance: The Case of Dutch Agriculture P.Diederen, H.van Meijl & A.Wolters Determinants of Innovative Activity in Canadian Manufacturing Firms J.Baldwin, P.Hanel & D.Sabourin Differences in Determinants of Product and Process Innovations: the French Case C.Le Bas & A.Cabagnols Differences in Determinants of Product and Process Innovations: the Spanish Case E.Martinez-Ros & J.M.Labeaga PART THREE: SPILLOVERS AND R&D COLLABORATION Innovation Without R&D? Public and Private Spillovers in the French Agro-Food Industry V.Mangematin & N.Mandran The Effect of Spillovers and Government Subsidies on R&D, International R&D Cooperation and Profits: Evidence from France F.Favre, S.Negassi & E.Pfister The Impact of Spillovers and Knowledge Heterogeneity on Firm Performance: Evidence from Swiss Manufacturing S.Arvanitis & H.Hollenstein Why do Firms Not Collaborate? The Role of Competencies and Technological Regimes A Leiponen PART FOUR: INNOVATION AND EXPORT PERFORMANCE Innovative Capabilities and Export Performance: A Study of Canadian Manufacturing SMEs E.Lefebvre & L-A.Lefebvre R&D and Export Performance: Taking Account of Simultaneity A.Kleinknecht & R.Oostendorp
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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 | 0.019 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".