Bibliographic record
Abstract
Introduction Chapter 6 outlined the framework for analysing historical processes in which institutional changes provided the causal linkages between episodes. Chapters 9 to 11 apply this framework to the historical record, showing it to be the appropriate formulation for modelling the post-World War II era. Historical events also suggest that, in an episode extending from the end of the nineteenth century to the end of the 1920s, performance-induced change in technology was the structural change linking this episode to the Great Depression of the 1930s. In this chapter, a technology version of our framework shows the linkages connecting the two episodes of the pre-World War II era. The data available to support analysis of this period are more limited than we would like. In contrast to the post-World War II period, comparably defined data describing the macroeconomic records of the developed capitalist economies are scant for the earlier era, making reliance on the American record necessary. However, the severity of this shortcoming is reduced by the existence of transmission mechanisms in the 1930s, so that events in the United States were quickly felt in the rest of the developed capitalist world. Consequently, an explanation of the events of the 1930s in the United States also provides understanding of events in other economies.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".