Stages of Learning Transport Terms in English on the Basis of Modern Technologies
Bibliographic record
Abstract
The development of technology and technology of material production, the processes of division of labor, its specialization and cooperation, and ultimately, the entire world economy, culture and language, as a means of human communication, storage and transmission of information, was greatly influenced by three industrial revolutions, which covered more than two hundred years of history. The first, the Industrial Revolution ( PR ) (from the last third of the 18th century to the last third of the 19th century), affected a limited number of countries: England (from the last third of the 18th century to the first quarter of the 19th century ), France (after the 1789 1794), Germany (from the 40s of the XIX century), the USA (after the civil war of 1861-1865), Russia (only after the abolition of serfdom in 1861), and Japan only by the end of the XIX century. For this reason, the Industrial Revolution was accompanied by the simultaneous expansion of the English technical language into other languages.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".