Bibliographic record
Abstract
This book is the outcome of a series of conversations which began in Cargèse, Corsica in 2001 between Jan Fagerberg and Louise Earl on directions for future research on innovation.The dialogue continued, expanded to include others and resulted in a research workshop hosted by Statistics Canada in Ottawa in October 2003.Many of the chapters in this book were first presented at the Ottawa workshop.Developing research programmes within institutions such as statistical agencies, policy departments and universities requires active participation in foresight exercises.Those that are successful bring together researchers with different backgrounds and research perspectives who bring cross-disciplinary insights to the table.The intent of this book is to capture these perspectives and provide insights for future research.One area of future research is improved understanding of the outcomes and impacts of innovation on the economy and society.Information and communication technology use has spread throughout the world.It is now possible to send instant pictures to handheld devices as events occur.People turn to the Internet as a source of information, to conduct research, to access government services, to make purchases and for entertainment purposes.The Human Genome Project has spurred the public interest in and debate of scientific knowledge.Genetically modified foods have brought biotechnology to the forefront and recently the press has begun to focus on nanotechnology.With the signing of the Kyoto Agreement, more countries and industries are looking at alternative sources of energies leading to the development of hybrid cars, ethanol gas production, and research into hydrogen power.These examples illustrate the interaction between the activity of innovation and the economy and the society.Innovation through technological change does not occur in isolation.It is linked to how organizations are managed and how they market their products.Innovation has impact on people, their societies, their cultures and the economies in which they live.Theorists, practitioners, empiricists and policy makers are all working towards developing an understanding of the complex interactions and relationships within systems of innovation.Spatial barriers between actors within systems of innovation are changing due to information and communication technology.However, these actors must still conform to current social and cultural norms of communication xvi
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.475 | 0.307 |
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".