Navigation, Commercial Exchange and the Problem of Long-Distance Control in England and the English East India Company, 1673-1755
Bibliographic record
Abstract
In this dissertation I address the related problems of expertise and long-distance control in the context of British navigation and the bureaucratic practices of the English East India Company. Expertise, in particular, is used as a framework from which I build outward to establish a stronger understanding of commercial trade, the circulation of knowledge and, most crucially, the place of the metropole. The first half of this dissertation introduces expertise and long-distances control and puts the concepts into historical context through the example of navigation between 1673 and 1755. Navigation is illustrative of the problem of expertise because it was a contentious subject at the time and, therefore, the contemporary debates can be followed. Expertise is a crucial problem because it directly addresses power and who controls knowledge. Thus, the question of navigational expertise ties directly to the problem of long-distance control. Therefore, my dissertation begins by moving outward from navigational instruction at the Royal Mathematical School to the practice of navigation on Edmond Halleys first Paramore voyage. \n \nIn the context of global commercial exchange, long-distance control became an increasing priority for those who sought to assert such control from a presumed centre onto agents around the globe. As such, the second half of the dissertation continues to follow actors further away from London with the setting moving to India and China where I contrast the idea of long-distance control with the reality. In practice the East India Company had little ability to impose itself on either its own employees or on the peoples with whom the Company wished to trade. Instead, the Companys efforts often drew attention to its ignorance of Asian trade and served to underline its weakness in the first part of the eighteenth century. The dissertation concludes by questioning the notion of the metropole and the periphery in the history of science and suggests an inversion of the traditional locations, with London now a periphery rather than centre, a state of affairs more in line with the situation at the time.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.027 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".