Bibliographic record
Abstract
There is a brief introduction explaining the themes in the literature available to date and how this thesis aims to add to available material. \n \nIn chapter one I give an account of early British research into nuclear science, including collaboration between British universities and the effect the MAUD Report had on accelerating the United States atomic programme. I introduce the main British scientists here . \n \nIn chapter two I focus on diplomacy between Britain and the United States in the period up to the Quebec Agreement. The two countries had their own atomic programmes at this stage and I discuss the lead up to the amalgamation of both programmes in August 1943. \n \nChapter three examines the British raids on German heavy water facilities and the efforts to stop Germany acquiring the means to make an atomic bomb before the Allies. Co-operation between the British and U.S teams at Los Alamos is discussed, along with the crucial role played by Britain in assisting the American scientists. \n \nThe British nuclear spies are featured in chapter four, focusing on Alan Nunn May and Klaus Fuchs. Their actions are discussed along with their arrests and trials. Effects of their cases on British atomic diplomacy with the Americans are highlighted. \n \nThe final section sums up the legacies of Britain¿s nuclear programme and its effect on British Cold War politics with America and the U.S.S.R. The fusion, or hydrogen, bomb is mentioned briefly and an overall assessment of the achievements of the British scientists is included.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".