Strategic innovation in small firms : an international analysis of innovation and strategic decision making in small to medium sized enterprises
Bibliographic record
Abstract
Contents: Preface 1. Strategic Innovation in Small Firms: An Introduction Tim Mazzarol and Sophie Reboud 2. National Innovation Systems Tim Mazzarol, Sophie Reboud and Jean-Guillaume Ditter 3. An Overview of the Survey Findings Tim Mazzarol and Geoffrey Soutar 4. The Flemish Creative Sector Ysabel Nauwelaerts, Frederik Van Assche and Ilke Van Beveren 5. Innovation Processes in SMEs: The New Zealand Experience Delwyn Clark 6. Designed in Italy - An Unrecognised Italian Innovation Model? Jane Klobas and Paola Bielli 7. The Context and Logic of Innovation at Two Small Enterprises: A Qualitative Analysis Hermann Frank, Manfred Lueger and Christian Korunka 8. SME Innovations: USA Assessment and Climate Results Newell (Sandy) Gough and Philip Olson 9. The Situation in Canada: Analysis of Canadian SME Innovation Behaviour Jacques Baronet and Johanne Queenton 10. The Situation in Switzerland Thierry Volery 11. The Situation in Australian Manufacturing Tim Mazzarol and Vijaya Thyil 12. High and Low R&D Intensity Firms in France Sophie Reboud and Tim Mazzarol 13. The Business of Biotech in Australia Genevieve Tattersall, Katherine Giles, Parbodh Gogna, Colin Tung, Shane Ridley and Tim Mazzarol 14. Conclusions and Lessons Learnt Tim Mazzarol, Delwyn Clark and Sophie Reboud Index
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".