Bibliographic record
Abstract
Tryggve Gran grew up in an affluent family in Bergen, Norway. The German emperor, William II, often visited the families of his friends. Gran became a good skier, hence well prepared for Robert Scott's second expedition to the Antartic in 1910. Gran deeply regretted the Scott-Amundsen competition, and was cut off from the team heading for the South Pole. In 1913, Gran trained in England and France as an air pilot. On 30 July 1914 he became the first pilot to cross the North Sea from Scotland to Norway. He joined the Royal Air Force in 1916 under the pseudonym of Teddy Grant, passing himself off as a Canadian, and received the Military Cross for distinguished war service. During the Second World War, Gran was a member of Quisling's pro-German National Party. A commemorative stamp was issued in 1944 on the 30th anniversary of his North Sea flight, and a meeting held in his honour with Quisling and German officers present. In this article, the author discusses some psychological aspects of Tryggve Gran's choice of tasks and of his politics. Gran lost his father when he was only five and when he was 11 he was sent off to a pension in Switzerland for a year. Strongly ambivalent feelings from the oedipal period and from the latency may later have been released through hazardous activities, certainly with self-destructive aspects. His membership in Quisling's party might be seen in this context.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.033 | 0.015 |
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".