Bibliographic record
Abstract
In part II, alternating episodes of poor and superior performance were traced to structural change endogenous to the performance of the economy, forming a causal chain linking successive episodes via negative feedback effects. The structural changes linking the episodes differed, with technology developments providing the link between the period of industrialization and the 1930s, and institutional developments constituting the links between the 1930s and the golden age and subsequently between the golden age and the existing age of high unemployment. Having made a point of the differences in linkages, we have no hesitation in stating that the failure to recover quickly once an adverse structural change has occurred is a failure of institutions to adapt to changing circumstances. As discussed in chapter 8, the depressed economic conditions extending over most of the 1930s could have been ended earlier with a Keynesian deficit spending programme, had there been the political will to introduce it. Although the cause of the Great Depression was technology, recovery was prevented by an institutional constraint on stimulative AD policy. In chapter 11 we argued that the golden age was brought to an end by the eventual incompatibility of full employment and acceptable rates of inflation. The institutional requirements for a full employment recovery today are more demanding than in the 1930s. Our programme for recovery is implicit in the commentary running throughout our explanation of the structural influences responsible for alternating episodes of poor and superior performance.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".