Innovation studies and knowledge generation
Bibliographic record
Abstract
Innovation started its journey as a central topic for research and teaching in economics and management a long time ago (Bontems, 2014;Pénin, 2016), but gained momentum during the final quarter of the last century (Freeman, 1997;Nelson, Winter, 1982).Innovation is not just a topic for scholars concerned with firms and industries, but also for those interested in public management, geography, investment, growth, and the global evolution of our society.Many believe that it should be the central topic taught on Economics and Management diploma courses, the other fields being an additive of innovation happening and diffusing its positive effects.Readers of the Journal of Innovation Economics & Management will certainly agree with such a position.In the last few decades, research in economics and management in the field of innovation, knowledge management and creativity has flourished.This Companion, edited by a team of leading scholars, reflects the variety of topics and the amount of knowledge and insights accumulated.The editors have published a substantial number of studies on the topics covered by this Companion.These range from knowledge management, the geography of innovation, communities, creativity management, routines, public -private relations, and so on.They are renowned for their work, thus they make a formidable team of editors for this Companion.In their introductory chapter, the four editors articulate the purposes of the Companion.They have produced a chapter where they give a broad overview of the evolution of innovation studies and how it ties up with other fields.To set the scene they present the evolution of innovation studies in eight parts, foreshadowing the general organization of the book (I.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.025 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".