Digital Additive Manufacturing: A Paradigm Shift in the Production Process and Its Socio-economic Impacts
Bibliographic record
Abstract
The most simple and sweeping proposition with respect to 3D printing is that it will change everything because it can print everything. 3D printing (also known as digital additive manufacturing) is a key driver behind the on-going paradigm shift from 20th century industrial production and economics to the 21st century post-industrial order defined by open-source collboration, intelligent, nanoscale and bio technologies. This paper examines four distinct characteristics of 3D printing that define and predict its revolutionary ramifications on manufacturing processes and the geo-economic contours of global trade. Digital additive manufacturing renders the established manufacturing process obsolete. 3D printing’s rapid diffusion is a consequence of its vast assortment of applications being freely available on open crowd-sourced websites. When one combines the ability and convenience of producing one’s own customized goods with the savings accrued through the elimination of labor, re-tooling, assembly, shipping and inventory carrying costs, the consequences are most ominous for any and all engaged in traditional manufacturing and dependent on the relative cost-efficiencies of out-sourcing. 3D printing not only renders factories obsolete but threatens whole country’s economies as production is taken up by the consumer and distribution is de-globalized.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".