Bibliographic record
Abstract
India and North America figured large in the Seven Years’ War. Here, the spatial linkage between macro- and microhistory is explored as news of the success of the Channel Campaign arrived in India and in North America. East India Company (EIC) officials felt concerned, conflict along the Carnatic Coast between France and Britain had started anew. For the first time, the Admiralty Office had ordered ships to protect EIC interests in the Indian Ocean. Admirals Charles Watson and George Pocock commanded a total of eight ships as France sought to avenge its losses in the Channel Campaign elsewhere in India. Meanwhile, North America filled the Duke of Newcastle with dread. George Washington’s loss at Fort Necessity, General Edward Braddock’s defeat in Pennsylvania, and Admiral Edward Boscawen’s failure to stop France from resupplying Canada added to the government’s concerns. But, Newcastle was more concerned about the political ramifications of North America and much of that had to do with trade. A monopoly, the EIC, controlled trade via the Indian Ocean. Trade with the Caribbean and North America, in contrast, was not monopolistic. Thousands of merchants would and did raise cackles at the ministry’s slow response to French threats seemingly everywhere.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".