Bibliographic record
Abstract
In 1845 Captain Sir John Franklin led a large, well equipped expedition to complete the conquest of the Canadian Arctic, to find the fabled North West Passage connecting the North Atlantic to the North Pacific. Yet Franklin, his ships and his men were fated never to return. The cause of their loss remains a mystery. In Franklin, Andrew Lambert presents a gripping account of the worst catastrophe in the history of British exploration, and the dark tales of cannibalism that surround the fate of those involved. Shocked by the disappearance of all 129 officers and men, and sickened by reports of cannibalism, the Victorians re-created Franklin as the brave Christian hero who laid down his life, and those of his men. Later generations have been more sceptical about Franklin and his supposed selfless devotion to duty. But does either view really explain why this outstanding scientific navigator found his ships trapped in pack ice seventy miles from magnetic north[unk] In 2014 Canadian explorers discovered the remains of Franklin's ship. His story is now being brought to a whole new generation, and Andrew Lambert's book gives the best analysis of what really happened to the crew. In its incredible detail and its arresting narrative, Franklin re-examines the life and the evidence with Lambert's customary brilliance and authority. In this riveting story of the Arctic, he discovers a new Franklin: a character far more complex, and more truly heroic, than previous histories have allowed. [A]nother brilliant piece of research combined with old-fashioned detective work ...utterly compelling. (Dr Amanda Foreman).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".