Measuring Economic Security in Insecure Times: New Perspectives, New Events and the Index of Economic Well-being
Bibliographic record
Abstract
This report has two main objectives. The first is to outline the development of the methodology for the measurement of economic security in the Index of Economic Well-being (IEWB) and to provide updated estimates of the Index of Economic Security over the 1980-2007 period for seven developed countries: Canada, Australia, Germany, Norway, Sweden, the United Kingdom and the United States. The four components of the economic security domain of the IEWB – security from unemployment, illness, single-parent poverty, and old-age poverty – are discussed. The second objective is to consider the adequacy of our framework for the discussion and measurement of economic security during times as tumultuous as the present. Since 2008, the global economy has fallen into recession and anxiety about the economic future has dramatically increased. In this context, how should one measure trends in economic security? Projections of the Index to 2010, computed on the basis of OECD unemployment forecasts, indicate that the global recession will lead to a substantial decrease in economic security as the recession continues.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".