A Geographic Analysis of Countries in Stage 4 of the Demographic Transition Model
Bibliographic record
Abstract
In 1929, Warren Thompson created an interpretation of the history of global human demographics that could theoretically predict the pattern that populations followed and would follow. This interpretation is now known today as the theory of the Demographic Transition Model (DTM). Broken down into 5 stages of varying rates of birth, death, and population change, the DTM shows how these three factors are predicted to eventually play out across the world given various factors. Stage 4 is characterized by a low, relatively stable population and low death rate coupled with a declining birth rate. Countries that are considered to be in this stage of demographic transition exist around the world. Yet, such countries are quite different from each other when it comes to geographic location, culture, and history. This project focuses on the following questions: What countries are in stage 4 of the Demographic Transition model, and how did they get there? Are there factors that these different countries share that may have pushed them into a stage 4 status in the twenty-first century? If so, are there any countries that are considered to be in stage 3 that will soon be in stage 4 due to these factors happening right now? Anticipated research discoveries include finding countries such as the United States, Australia, Singapore, Canada and most of Europe in stage 4 of the DTM. One of the main causes for these countries to be in stage 4 may be the introduction and the social acceptance of family planning resources. The increase of industry, the decrease for the need of agricultural-based lifestyles, war/political events and the development of modern medicine are also likely leading factors contributing to a stage 4 status.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".