Bibliographic record
Abstract
Through their goals, the macroeconomic policies aim to the near or farther future, this being the reason for which evolutions have to be anticipated. Deliberately or not, the decision-makers continuously operate with mental schemes of prospective nature. Many times, these procedures are purely empirical. But, no matter how much 'trained', the intuition has its own limits that can be overcome only through rigorous modelling techniques. Modern economy management placed itself unequivocally on the second path. This impulse, together with the progress in macroeconomics and computational techniques formed the background for the spectacular development of macroeconomic modelling in the second half of the 20th century. There are many data banks for macromodels. One of the most comprehensive seems to be the one built and continuously updated by the Hamburg Institute of Statistics and Quantitative Economics. In mid 2001 there were around 4500 such models (see Appendix 1), an amount - we must admit, impressive - that indicates the very high interest in the entire world in this instrument of analysis and forecasting. The first place was held by United States, with 495 models, but also other developed countries were recorded with important figures: Germany (Federal and former Democratic together) - 343, United Kingdom - 213, Japan - 207, France - 152, Italy - 130, Canada - 126, the Netherlands - 122, etc. In other regions, including the Central and East European countries, modelling also expanded significantly. In fact, since 1967 - under the aegis of the United Nations Organization - the LINK Program is carried out, which promotes this technique at world level, with the participation of well-known specialists. The current paper aims to examine the following issues: A. What is an economic model? B Economic models typology, C. The sequences of the numerical modelling process.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.049 | 0.024 |
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".