Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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;Pnin, 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.Innovation
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it