From science to technology : The value of knowledge from the business sector
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
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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