Wielding scissors skilfully: The matching process of advanced materials ventures
Bibliographic record
Abstract
This paper examines the industrial incentives for commercialising advanced materials and, in particular, nanomaterials with reference to issues raised in the technology strategy and technology entrepreneurship literature. We draw on longitudinal empirical data to show that smaller and newer firms are playing an increasing role in the commercialisation of advanced materials innovations. However, new technology based firms face substantial barriers to commercialisation, including access to the complementary assets of large firms and institutions. To illustrate these challenges, we examine a case study of a start-up firm commercialising carbon nanotubes. Through use of an open systems model, we characterize their alliances and interactions in attempting to commercialise their products in several markets. This analysis illustrates the daunting challenges facing start-up firms as they attempt to commercialise advanced materials innovations. The most difficult challenge appears to be one of prioritisation of development objectives and, subsequently, of alliance building. Proposed policy recommendations focus on supporting the entrepreneurial process of matching technology resources and alliance-building with market opportunities.
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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 | 0.010 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".