Logistic oscillator model for gross domestic product
Bibliographic record
Abstract
In this paper, a logistic oscillator model is presented to analyze the economic cycles of five selected economies: Mexico, Brazil, Canada, China and the United States. This selection was made taking as reference their level of economic development and their geographical position. The proposed model is an extension of the production Phillip’s model (1959), which considers autonomous expenses dependent on time. It should be noted that the logistic oscillator combines the dynamics of a forced damped oscillator, whose restoring force incorporates Verhulst’s logistic equation. The data used are the production levels of The Organization for Economic Cooperation and Development (OECD) at nominal prices of the mentioned nations. The results obtained show terms of no economic damping with explosive tendency. China shows greater nondamping with an explosive trend, as does Mexico. The countries with the greatest oscillatory behavior are Brazil and Canada. Additionally, those showing exponential dynamics are China and the USA. The fitting of the logistic oscillator to the data is significant given the level of the determination coefficient. Therefore, the results indicate that the model can be useful in formulating economic policy criteria, since it allows one to predict the evolution of the economic cycle in the future.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".