Noah and Joseph Effects in Government Budgets: Analyzing Long‐Term Memory
Bibliographic record
Abstract
This article examines the combined effects of what mathematician Benoit Mandelbrot has termed “Noah” and “Joseph” effects in U.S. national government budgeting. Noah effects, which reference the biblical great flood, are large changes or punctuations, far larger than could be expected given the Gaussian or Normal models that social scientists typically employ. Joseph effects refer to the seven fat and seven lean years that Joseph predicted to the Pharaoh. They are “near cycles” or “runs” in time series that look cyclical, but are not, because they do not occur on a regular, predictable basis. The Joseph effect is long‐term memory in time series. Public expenditures in the United States from 1800 to 2004 shows clear Noah and Joseph effects. For the whole budget, these effects are strong prior to World War II (WWII) and weaker afterward. For individual programs, however, both effects are clearly detectable after WWII. Before WWII, budgeting was neither incremental nor well behaved because punctuations were even more severe and memory was not characterized by simple autoregressive properties. The obvious break that occurred after WWII could have signaled a regime shift in how policy was made in America, but even the more stable modern world is far more uncertain than the traditional incremental view.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".