Bibliographic record
Abstract
Introduction Human biologists, especially those studying demography and disease in past populations, have long used historical archives to learn more about the populations they are studying. A variety of historical and statistical techniques have been used to analyze and interpret data derived from the archives. Mathematical and computer modeling are approaches that have only rarely been used, but they can provide interesting and valuable insights not generally possible with other methods. In the following I first discuss the nature of models in general and mathematical models in particular. This is followed by an introduction to several of the most commonly used approaches to mathematical and computer modeling in the social sciences and a selective overview of the use of these approaches to study infectious diseases in human populations. Finally, I illustrate in some detail the process of model development and analysis, drawing upon work I have done in conjunction with Ann Herring on the spread of the 1918–19 influenza epidemic in central Canada. What is a model? People often use the word ‘model’ very loosely and in different ways. To some degree, the lax usage of the word reflects both real ambiguities in the concept and the use of one word to designate several related ideas. At the most general level, any model can be thought of as an object or concept that is used to represent something else. Models simplify reality and aid in determining the role and importance of factors that influence the real world.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".