Public and private goods in the development of additive manufacturing capacity
Bibliographic record
Abstract
Abstract The promotion of additive manufacturing (AM) as a set of enabling technologies has been a prominent feature of new policies seeking to revitalize manufacturing in developed economies. Because of its differences from traditional manufacturing technologies, small businesses, in particular, face high costs in adopting AM methods. How can governments assist small firms and their innovation ecosystems to make significant leaps in enabling technologies? This paper conceptualizes the challenges faced by groups of small enterprises adopting new technologies and a decentralized policy effort to systematically increase the use of advanced manufacturing technologies. In Canada, funding used by community colleges to create applied research centers has been intended to establish anchors for local “industrial commons” around advanced manufacturing methods. By providing both information and working capital to private sector partners, these community college programs should ideally mitigate challenges to the adoption of AM technologies—the so-called “valley of death”—in local ecosystems. There are many successful individual cases of partnership (i.e., private goods); however, this bottom-up approach seems to fail both as a means of promoting vibrant industrial commons (i.e., public goods) and as a coherent national strategy. We trace the challenges of this approach to principal-agent problems associated with layering new programs upon existing organizations, the density of program participants, and the presence of appropriate technologies.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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 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".