Bibliographic record
Abstract
The subject of mortality is a meeting ground for diverse disciplines, and hence it is not surprising to see it being approached from remarkably different perspectives.The book has 13 chapters, covering a wide range of topics, using data from different geographic areas.The chapters can be grouped into three broad themes: mortality estimation and projections (chapters 2, 3, and 5), explanation of trends in mortality and causes of death (chapters 4, 8, 9, 11, and 13), and measurement of impact of determinants (chapters 6, 7, 10, and 12).Chapter 1, by Jon Anson and Marc Luy, adequately summarizes all the chapters in the book.It is clear that the aim of the book is to put together 'state of the art' methods used in mortality and morbidity.However, it is in the chapters dealing with mortality estimation and projections that "cutting edge" methods are used.The remaining chapters, interesting as they are, used methods that would fall under "normal science" rather than cutting-edge methods.Chapter 2 is authored by Peter Congdon, a pioneer in the analysis of small area mortality and the author of books on Bayesian statistical modelling.In this chapter, he exploited correlations between adjacent ages and areas with Bayesian modelling and applied it to data of over 3,000 US counties.He found that "whereas there is little gain in life expectancy in the lowest income counties, high income counties showed expectancy improvements exceeding the US average."This new approach is an improvement on standard conventional life table methods used in small area mortality that overlook spatial or age correlations.Chapter 3, by Joroen Spijker, is clearly the most ambitious chapter in the book.He uses data from 21 countries over the period from 1980 to 2000 to model death rates for 11 causes of death.The model used allows for the simulataneous analysis of inter-country and inter-temporal variations in mortality.As a departure from other models based on extrapolation, this model included data on some known socioeconomic determinants of mortality.The model was validated and then used to produce short-term projections of rates due to causes of death.This is a significant contribution in an area that is still in its youthful stage of development.Chapter 4, by Katalin Kovács, thoroughly reviews the different variants of Epidemiological Transition Theory and the Nutritional Transition Theory.Using causes of death data from Hungary, Kovács tries to group the different causes of death in such a way as to allow her to see the role of the different theories in explaining inequalities in mortality between the less educated and the more educated.Her conclusion was that "nutrition transition theory provides a very plausible explanatory framework for the growth of mortality inequalities."Chapter 5, by Sarinapha Vasunilashorrn and others, attempts to predict mortality from profiles of biological risk and performance measures of functioning.They were able to get a rich set of data by linking a national US survey data with the causes of death data contained in the National Death Index.According to the authors,
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".