Economic-Political Cyclicality or: Is There Any Good in Economic-Political Cycles Theory?
Bibliographic record
Abstract
This article deals solely with the analysis of economic-political cycles during the past decade. The reader will find here a limited attempt to point out the present status of the arena while it is used as an analytical tool. This article offers a theoretical examination of the concept economic-political cyclicality and discusses the development of patterns of thinking related to it. The article presents the main principles of the idea, explains the basic assumptions, which the various approaches to it have in common, and points out the differences of opinion among the leading theorists. The analysis shows that there are numerous limitations of the theoretical tools and that great caution should be used in regard to conclusions arrived at on the basis of analyses that use these instruments. In this area, as in other areas of study that bring together economics and politics, a great deal still needs to be done. We argue that whereas those who deal with business cycles the attempt are always the same. Simply stated, they deal with specific events in which decisions are made for the purpose of achieving the maximum political-social gain in (conscious) exchange for conceding optimum macro-economic gain.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.013 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".