Bibliographic record
Abstract
Introduction Capitalism's golden age can be characterized in several ways. Unemployment rates were at historical lows and growth rates of labour productivity and per capita incomes in most economies were at historical highs. In the first instance this was attributed to strong and growing aggregate demand pressures. These buoyant conditions were attributed in turn to the absence of constraints on aggregate demand. During this period there were institutions in most OECD countries that relieved the authorities of any real or perceived need to hold AD below full employment levels. These institutions permitted the simultaneous achievement of other goals, e.g. low inflation, external balance. To a large extent the formation of golden age institutions was causally linked to the performance of the economies in the 1930s and 1940s, demonstrating evolutionary and hysteretic processes with negative feedback. The main task of this chapter is to explain the poor unemployment performance in the episode since the golden age also as the outcome of evolutionary and hysteretic processes with negative feedback. This includes both the impact of institutions on performance and the impact of performance on institutions, to establish a causal linkage between the golden age and the present episode. We argue that high and rising unemployment in the current episode can be traced ultimately to a marked change in institutions, which was to a very large degree induced by the performance of the economy in the golden age.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".