From science to technology : The value of knowledge from the business sector
Bibliographic record
Abstract
Expansion of government R & D budgets on promoting electric vehicle (EV) adoption and charging infrastructure development is likely to continue to be a key component of ecological innovation policies. Using an original data set of non-patent literature (NPL) references extracted from patent documents pertaining EV charging technologies, this paper provides new evidence on the flows of knowledge with or without a scientific contribution from the business sector. Three main questions are addressed in this paper for measuring the value of knowledge produced by firms, which not only contributes towards a better understanding of EV but serves the purpose of fostering more partnerships and unlocking further investments in research. First, what information is most useful to the technological development? Even firms are increasingly encouraged to engage in EV innovation process, a relatively profound influence on knowledge transfer has not be exercised, especially in generating applied technologies measured by redefined average NPL citation compared to academic institutions. Patents with firm NPL have a special focus on inorganic chemistry and nanotechnology except as common issues identified related to climate change mitigation and energy storage. Second, which kind of firm’s contribution produces the most valuable research? The university-firm research collaborations have captured more attention from science to technology while knowledge produced solely by firms has been transferred to a broader distribution in geography. Finally, how scientific knowledge is commercialised? Patents with firm NPLs, in particular the one regarding networked infrastructure and energy generating have been transferred more frequently to companies and universities residing in the US, Japan, Canada and Germany between 2010 and 2014. However, patented technologies of electrical distribution network and charging batteries with non-firm NPLs are mainly assigned to companies in France and Korea between 2008 and 2013. The role of firm in knowledge and technology transfer needs to be further explored in a border technological field notwithstanding the gaps in NPL citation compared to academic institutions.
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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 | 0.006 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".