"COMMERCE DES LUMIÈRES": THE INTERNATIONAL TRADE IN TECHNOLOGY, 1763-1815
Bibliographic record
Abstract
It is well known fact that the peace treaty signed in Paris in 1763 which brought to a conclusion the Seven Years' War inaugurated the great age of the Grand Tour.For the next three decades Europeans were able to indulge in land and sea travel largely unhindered.The ramifications of the colonial struggle in North America may have diminished the flow of continental travellers crossing the Channel for a time between 1778 and 1781, but this episode seems only momentarily to have caused cultural exchange within Europe's intelligentsia to falter.Less well known is the fact that the final quarter of the eighteenth century also witnessed a remarkable growth in the practice of industrial tourism.Irina and Dmitri Gouzevitch 1 have drawn attention to this phenomenon, as have I in my own published work 2 .Travel specifically in order to make contact with entrepreneurs and to view industrial premises was a practice driven by a number of considerations.This paper will concentrate mainly on what we can learn about the motivations of travellers, whether admitted openly or concealed. IWe need to begin with the broader picture, however.The practice of continental travel -whatever its objective-provides evidence that an important cognitive shift was taking place during these decades.Edward Gibbon (1737-1794) recommended that those embarking on a foreign journey should equip P M .
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".