Bibliographic record
Abstract
This chapter looks at the recent Great Recession using the lens of the Index of Economic Wellbeing and available data for the period 1995 to 2010 from 14 countries: Australia, Belgium, Canada, Denmark, Finland, France, Germany, Italy, Netherlands, Norway, Spain, Sweden, the United Kingdom, and the United States. It makes three main points. First, any aggregate index of wellbeing necessarily imposes some weighting of the components of wellbeing. This implies that calculations of trends in aggregate indices can be sensitive to the weighting of components when trends in those components of wellbeing differ, as was the case across these 14 nations in the 2007–2010 period. Second, wealth stocks are accumulated over many years, and the institutions that determine the distribution of income have great inertia within countries. Hence, in normal times neither of these dimensions of economic wellbeing is very sensitive to year‐to‐year variations in output or employment within countries. By contrast, annual consumption flows and measures of economic security are much more sensitive. Third, countries differ a lot in the degree to which economic security and consumption flows vary with year‐to‐year fluctuations in output and employment. Some countries' institutions are clearly much more effective than others in insulating economic security and average consumption from cyclical volatility for any given size of shock.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".