Navigating the Pacific from Bougainville to Dumont d’Urville: French Approaches to Determining Longitude, 1766–1840
Bibliographic record
Abstract
Defeated in the Atlantic during the Seven Years’ War, the French turned to the Pacific with the hope of finding new lands and markets that would redress the balance of power so grievously disturbed by the expansionist energies of perfidious Albion. France, however, faced the same problem as its rival in venturing into what was, from a European perspective, largely a new quarter of the globe. Navigating the Pacific magnified across a third of the Earth’s surface the problem of locating one’s position with exactitude; in particular, it required determining longitude at sea. The means to do so had been an increasing preoccupation of both the British and French states and their associated scientific establishments. As Danielle Fauque and Guy Boistel show in this volume, various French techniques for solving this problem had been recorded before the deployment of John Harrison’s epochal invention, his sea watch ‘H4’, in 1761. The conclusion of the Seven Years’ War in 1763 was, however, to lead to a fruitful interaction between both nations’ attempts to solve ‘the longitude problem’. 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.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".