Overcoming high mortality of innovative ideas (‘valley of death’) for scientific and educational environment: a bibliometric analysis
Bibliographic record
Abstract
The study focuses on the problem of the gap between innovative ideas and scientific discoveries and their practical implementation and commercialization. The purpose of the article is to analyse the scientific basis and main tendencies of the issue of high mortality of innovative ideas in scientific and educational environment (‘Valley of Death’). Therefore, the bibliographic analysis of published scientific articles on the mortality of innovative ideas is conducted. The methodology is based on data analysis from the Scopus scientometric database and R-Studio software. At the first stage of the study, a research infrastructure was created using the scientometric database Scopus and an analysis of literary sources was carried out. The second stage of the study focused on bibliographic analysis of the scientific documents indexed by Scopus over a 20-year period (2003-2023). This period was chosen to capture developments in the field, identify evolving trends, and trace how different aspects of the Valley of Death have been studied and addressed over time. The chosen keyword was "Valley of Death in innovation" (172 documents). This analysis also covered the study of the network of collaboration between authors, the publication trends of scientific papers, the estimation of the average number of citations of papers and the discovery of a thematic map. The resulting visual information was a valuable tool for studying current patterns. The results of the analysis indicated an interest in the mortality of innovation processes and a growing number of scientific literatures on this topic. It was found that this research problem started a long time ago but gained significant interest after 2010. The largest number of published works belongs to the USA. Research on international cooperation also shows that many countries are well integrated into scientific activities. A global science collaboration between the USA, Germany, Canada and the UK facilitates the sharing of knowledge and resources to advance innovation. The obtained results highlight the importance of developing sustainable strategies (the key strategies were proposed and described) and building networks between academic institutions, businesses and governments to overcome the "Valley of Death". This study can be used as a basis for further scientific research in this field.
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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 | 0.010 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.058 | 0.083 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".