Bibliographic record
Abstract
We generally think of mass production as a uniquely twentieth-century phenomenon. However, its evolution can be traced back much further. The explosion in printed books, following Johannes Gutenberg’s fifteenth-century development of the Korean invention of movable type, had an impact on human society of heroic proportions. Precursors of modern mass-production, based on the specialization of labour and the use of specialized machinery to ensure a high degree of uniformity, can be traced to the eighteenth century. Writing in The Wealth of Nations in 1776, Adam Smith used the manufacture of pins to exemplify the improvement in productivity resulting from the utilization of uniform production techniques. Today, every conceivable sort of commodity is mass-produced. Pills, paints, pipes, plastics, packages, pamphlets and programs are mixed, extruded, poured, forged, rolled, stamped, molded, glued, printed, duplicated and dispatched worldwide on an immense daily scale. The most successful modern products are an amalgamation of many disciplines, years of experience, careful execution, rigorous production control and never-ending refinement. In no other industry is the cross-disciplinary matrix so tightly woven, and the number of interacting elements so incredibly high, as in the semiconductor business. Reaching back to Gutenberg, and drawing on the principles of photography pioneered by Daguerre in the 1830s (embracing optics, lens-making, photosensitive films and chemistry), transistors are defined by a process of lithography, which is essentially printing. But what eloquent printing this is! A 200-mm silicon wafer has a useful area of about a little less than a page of this book containing some 400 words of text, equivalent to perhaps 16,000 bits. However, when divided into chips the size of a modest microprocessor, today containing about 50 million transistors, through perhaps 20 successive layers of printing and processing each wafer generates some 10 billion devices in a single mass-produced entity. In a production lot containing 40 such wafers, some 400 billion tiny objects are manufactured in a single batch. Multiply this by the daily manufacture of integrated circuits worldwide, and it will be apparent that the number of transistors that have been produced Barrie Gilbert
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.243 | 0.192 |
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".