Bibliographic record
Abstract
Summary form only given. The main theme inherent in this paper is that technological capabilities are not fully understood and remain understudied as a field of inquiry. As a proxy measure of such capabilities, a family of related patents may be viewed as the revealed manifestation of technological capabilities (RTC) at micro level. Patent databases have stored a wealth of publicly-held and verified knowledge. Obviously, this type of publicly-accessible and -protected indicators can be used to detect intellectual/technological capital in competitive environments. No individual, company, or country should ignore this invaluable competitive information in its research frontier. Before investing in a search for, and inclusion of, for example, tacit knowledge, embedded knowledge, embodied knowledge and so on, one must start with this verified knowledge. Moreover, it would be waste of time and effort, if such indicators of revealed technological capability knowledge are not cost-effectively data-mined prior to any large-scale research. Therefore, no patent or patent study should be ignored by any competitor with related ideas, as any patent can then act as the hidden "root system" for supporting the further growth of a "tree" and its new branches. When the full grown tree becomes visible, it is to late. Therefore, the data-mining or text-mining (and their further refined versions) should be utilized to reveal the existence of knowledge about any technological opportunities and/or research frontiers, however rudimentary prior to any related undertaking. This paper provides a methodology and examples for such an approach.
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.
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.012 | 0.031 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 0.005 |
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".