Bibliographic record
Abstract
A crucial point about Koransha and the other firms briefly discussed in the final section of Chapter 5 is that, having been established as modern corporations some time during the period from the last quarter of the 19 th century and the first decade of the 20 th , they are still up and running as viable LMEs having made it to the 21 st century. This infers a lot about the organization of the industrial structure in Japan and the arrangement of firms as discrete performers within that structure, as well as about how technology is dispersed and how it is acted upon and by whom. The shape of this organization and the implications of technology dispersal were to take on clearer definition in the second period, that following the end of the Meiji era in 1912 through to the culmination of the Second World War. So we now turn to how the samurai and the artisan confronted and contributed in their respective ways to this new phase, and then to the extent to which they merged their efforts, albeit haltingly and imperfectly, to meet the common challenges. Emerging from this activity, it will be argued, is a further substantiating stage in the evolution of the Japanese LME. This chapter explores the unfurling of events to the start of the 1930s, while the following chapter looks at the charged atmosphere of the nation on a war footing up to 1945 and the further implications this had for the industrial structure and the LME within that structure. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".