Bibliographic record
Abstract
Abstract Over the past three hundred years there have been countless attempts by governments of all types to control fertility and reproduction. Currently, more than 170 countries representing over 85 percent of humanity are actively trying to engineer how many children a person will have. Democratic, authoritarian, religious, secular, Western, Eastern, and African states have all tried with little success to control individual fertility decisions. This presents a series of interesting puzzles. Why do governments want to control childbearing decisions? What are they trying to achieve? Moreover, almost all attempts to control fertility have failed. Policies rarely, if ever, achieve government objectives. Accordingly, why do policies so routinely fail? Why do governments of all shapes and sizes continue to create policies that have a robust record of failure? What accounts for such unusual cross-national trends in government attempts to instill a sexual duty to the state? This book fills the gap by analyzing the origins, growth, and development of fertility as a national and international political issue; the rise and fall of the discourses used to ascribe meaning to natality; and the global proliferation of isomorphic policies adopted by widely dissimilar states. It proposes an explanation for the widespread failure of hundreds of years of policy.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.006 |
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".