Hvorfor Sudbø-saken bare ble en sak om Sudbø : En drøfting av Sudbø-saken med utgangspunkt i Vancouver-plakatens forfatterskapskriterier
Bibliographic record
Abstract
Why The Sudbø-story only was as a story about Sudbø\n\nA disscusion of the Sudbø-case with basis in the Vancouver-group requirements for authorship\n\nABSTRACT:\n\nICMJE revised the uniform Requirements for manuscripts submitted to biomedcal Journals february 2006 only one month after the so called Sudbø-story. The Sudbø story made headlines way outside the medical reserach environments. The medical doctor and dentist, Jon Sudbø confessed that he had manipulated data in his extensive research on oral cancer january 2006. This made a huge debate about ethical practise in medical research spring 2006. The manipulated research was first discovered in a Lancet article Sudbø and 13 other authors published autumn 2005. All of Sudbøs research through 13 years was then examined by a inquiry appointed by Radium- og Rikshospitalet and the University of Oslo. The Inquiry concluded that nearly all of Sudbøs research had to be withdrawn including his doctor thesis. Shortly after his liscences as a doctor and dentist was also withdrawn.\n\nI want to discuss how the medical ethical framework functioned and eventually not functioned in this certain case. Primary through the Vancouver rules point IIa about authorship and contributorship. With the Inquirys report of 30.06.06 about the Sudbø-case as a basis I want to study the Vancouver-rules usefulness and benfit as a normative ethical tool for medical researchers and editors questioning authorship and contributorship. \nTheoretically the vancouver rules could be understood with basis in ethics of consequences , I will therefor try to discuss it within that 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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.009 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".