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
Technology transfer is a business process not a techni- cal one. It should, however, only be undertaken by people with a solid technical understanding of how the technology works and can be applied. Ideally technology transfer occurs because of market pull, not a push. Selling a technology that nobody needs, wants or under- stands is bound to fail. At the outset of a project, you have a need, i.e., a problem that needs solving, which is conceptualized as part of 'whiteboarding' a solution. At this point, the technol- ogy transfer process needs to be engaged. The technology trans- fer process looks to bring existing technologies into the project to save development costs and explores market alternatives for the project outcome to increase profit. If it is determined that a project can benefit from the transfer of a known technology, a market pull has been initiated. If an alternate use of technology is obvious, then a market technology push is initiated. Push, however, is only one side of the equation and requires a user pull to create a balance. Technology transfer is as much a process of incorporating an available technology as it is adapting the technology to a new application. Technology transfer is motivated by an organiza- tion's need to generate new revenue, reduce costs or both. Or- ganizations can save in two ways: (i) sunk cost of development can be distributed over more than one product; (ii) sunk cost of development can be reduced by making use of technology that has already been developed. Technology transfer applies to both new and active projects. Applying technology transfer processes early in a project rather than attempting to insert it later in the development cycle has the potential to lower development costs and increase the return-on-investment of sunk research and development costs. I. TECHNOLOGY TRANSFER - STATUS QUO
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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