Bibliographic record
Abstract
Sweden lost its World Cup quarter final to England in summer 2018, but it has certainly met its goals for infant survival. Child mortality is almost twice as high in England as Sweden, according to a study by Zylbersztejn et al 1 that covered 2003–2012. This is a little difficult to understand given the many similarities between the two countries and the fact that Sweden essentially copied the model for the UK National Health Service. However, several reasons could explain the difference. In the 18th and 19th centuries, Sweden was a poorer country than, for example, France and England, and it had very high infant mortality rates. In 1845 the Swedish government appointed Fredrik Theodor Berg (1806–1887) as the first professor of paediatrics in Sweden, and possibly the world, at the Karolinska Institutet in Stockholm. He stated prophetically that “the health of the nation is related to the survival of infants.” Crucial political reforms led to decreasing infant mortality in Sweden during the 20th century.2 Antenatal and children’s clinics were established and poor mothers could receive child benefits from the 1930s. Sweden’s economy was boosted after the Second World War, partly because the country had remained neutral. The Swedish government also introduced social reforms and considerable improvements in housing for families with children. Most of the poverty and the small midwifery units like Nonnatus House in the British television series …
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.056 | 0.013 |
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".