Bibliographic record
Abstract
Current historiography of the rebalancing of the British Empire’s global strategy in the 1920s is incomplete. Their strategy was built on protecting Imperial global maritime trade, itself based on returning global mobility to the fleet and building a fleet base at Singapore to block Japanese fleet entry into the Empire’s demographic heartland of the Indian Ocean. This was a Corbettian seapower strategy, now obscured by the events of 1941-42. This article examines the nature of the Singapore strategy as it was developed to allow a new oil-fired fleet to operate as globally as the pre-war coal-fired fleet. L’historiographie actuelle du rééquilibrage de la stratégie mondiale de l’Empire britannique dans les années 1920 est incomplète. Cette stratégie reposait sur la protection du commerce maritime mondial impérial, lui-même fondé sur le rétablissement de la mobilité de l’escadre à l’échelle mondiale et la construction à Singapour d’une base pour l’escadre visant à bloquer l’entrée de l’escadre japonais au cœur démographique de l’Empire de l’océan Indien. Il s’agissait d’une stratégie corbettienne en matière de puissance maritime, ayant été depuis éclipsée par les événements de 1941-1942. Le présent article porte sur la nature de la stratégie de Singapour telle qu’elle a été élaborée pour permettre à un nouvel escadre alimenté au mazout d’exercer ses activités à l’échelle mondiale tout comme le faisait l’escadre alimenté au charbon d’avant la guerre.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".