Bibliographic record
Abstract
In both rich and poor nations, worrying about future economic dangers subtracts from the present well-being of individuals, which is why affluent societies have complex systems of private insurance and public social protection to reduce the costs of economic hazards. However, the citizens of poor nations (i.e. most of humanity) typically find both private insurance and public social protection to be largely unavailable – their lives are both poorer and riskier. How can one measure economic insecurity in these very different contexts? Because rich nations have better, more easily available data, Section 2 illustrates the measurement of economic insecurity and its importance to trends in relative economic wellbeing in four affluent OECD countries between 1980 and 2009. Section 3 then uses available data to estimate the level of economic security in approximately 2008 in a comparable way in a broader sample of countries. To reflect better the reality of developing countries, it: (1) includes the volatility of food production in the risk of loss of livelihood; (2) adjusts the risks of health care costs to consider the proportion of household spending on food (which is non-discretionary, and large in poor countries) and (3) adds adult male mortality to the risk of divorce in calculation of the risk of single parent poverty. Section 4 discusses some implications and concludes.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".