Titanium: the innovators' metal - Historical case studies tracing titanium process and product innovation
Bibliographic record
Abstract
This paper examines innovation in relation to the a vailability of a new material, specifically the metal titanium. The paper aims to highlight the need for the inclusion of entrepreneurial innovation as a necessary focus are a in the development of a titanium metal value chain. Both the Department of Science and Technology (DST) and the Department of Mineral Resources (DMR) have identified the creation of titanium metals production capabilities as a key growth area for South Africa. Using historical literature as a source of data; the activities of selected innovators who use d titanium metal as a central component in their success were investigated. The origin of t he process innovation behind the titanium metals industry, and two titanium product innovatio ns: namely, medical implants and sporting goods were detailed as case studies. It wa s found that individual innovators were responsible for the creation and rapid growth of th e titanium industry and responsible for the development of titanium product applications. T here is then identified a need to link the current research and development into the titan ium metal value chain with individuals and organisations that actively commercialise innov ative processes and products.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".