Bibliographic record
Abstract
Every first-year demography student learns about the demographic transition theory, a framework used to represent the processes of transformations of societies characterized with high birth and death rates to ones with low birth and death rates, in conjunction with economic development.The theory mainly draws on hypotheses inspired by observation of broad trends having occurred at various times across different regions of the world.It has its proponents given its generalizable approach to explaining global demographic trends, but is notoriously deficient as a model for understanding the causal effects between economic development and fertility outcomes, predicting future demographic trends in a given society, or describing individual cases.Decades after the first scientific publications on demographic transition, demographers -both established researchers and the upcoming generation -continue to study and debate the interrelationships between fertility and economic development, notably in the context of persistent high fertility and extreme poverty in parts of the developing world.Conclusive evidence on a critical catalyst to fertility decline in a country remains elusive.In the book Poverty Reduction -An Effective Means of Population Control, the author takes a strong stance that "it is poverty that increases fertility, not the other way around."Mohammed Sharif, professor at the University of Rhode Island, argues that this conclusion is reached based on review of the literature on population policy and fertility behaviour of the poor in developing countries, combined with statistical testing of the "irrationality" hypothesis of population policy using data from multiple countries.He starts from the premise that policy makers assume that the poor do not make their decisions rationally.He presents ten chapters using various arguments and empirical analyses to support his contention.He claims his ultimate goal, shaped in part by his personal experiences
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 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".