Bibliographic record
Abstract
Editor—Meadow has sought to overcome criticism of the misapplied statistics in the trial of Sally Clark by his own version of the evidence.1 He has missed the point. His belief in her guilt does not justify misplaced evidence. Watkins addressed the issue of presenting statistical evidence fairly.2 He concluded that doctors should not use techniques without acquainting themselves with the principles underlying them. Meadow blames biased media reporting while advancing his own interpretation of the prosecution evidence in a manner that leaves little room for doubt. Yet such a strong bid to minimise the influence flawed statistics may have had betrays certainty in a field where there should be considerable room for doubt. I do not know the details of the case, but the mere fact that many medical witnesses were called by both the prosecution and the defence indicates the evidence was not clear cut. Unless Meadow is the victim of some conspiracy by the press, the edition of the Observer published the day after the BMJ gives details of the defence evidence, suggesting his paper is anything but a balanced interpretation.3 The power of medical opinion in judgments is quite profound, so it is not possible to know what influence the misleading information had on the outcome of the trial. It is crucial that opinion must at all times be accurate and evidence based if miscarriages of justice are to be avoided. Essential criteria have been set down for expert opinion,4 and require the expert to: Provide a straightforward and not misleading opinion Be objective and not omit factors which do not support their opinion Be properly researched. When belief systems become rigid to a degree where perceived guilt is put forward as justifying bad evidence, the concept of an independent and impartial adviser to the court is lost. This undermines the court process and the principles of natural justice as required under article 6 of the Human Rights Act.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.030 | 0.031 |
| Insufficient payload (model declined to judge) | 0.005 | 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".