Bibliographic record
Abstract
We should not indulge in high hopes of producing rapidly results of immediate use to economic policy or business practice. Our aims are first and last scientific. … The only way to a position in which our science might give positive advice on a large scale to politicians and business men, leads through quantitative work. For as long as we are unable to put our arguments into figures, the voice of our science, although occasionally it may help to dispel gross errors, will never be heard by practical men. Joseph Schumpeter [1933, p. 12] The … fundamental point is that it will be necessary to distinguish between ‘forecasting’ and ‘prediction’. Forecasting will be limited to the extrapolations based on empirical models or data exploration, whereas a prediction will be formed from a theoretical model. Clive Granger [2012, p. 312] It’s tough to make predictions, especially about the future. Yogi Berra Economists have been building econometric models for several decades. Econometric models by design attempt to capture or represent simultaneously the data of interest and the ideas about how the modeled economy functions as reflected in that data. The primary task is one of identifying a set of parameters or constants that can be seen to characterize the economy being modeled. Such models are thought to represent a cause and effect relationship between two types of observable quantities. The effects are the endogenous variables being explained and the causes are the exogenous variables whose values are determined either by nature or by public policy autonomously but not necessarily independently of the model. Truly exogenous variables are variables determined by events beyond anyone’s control or by prior artificial constructs that by design are completely within the control of governmental policy makers (e.g., tax rates, subsidies, etc.). It is the posited fixed parameters that ultimately determine the effects of any changes in the causes. The hope has always been that econometric model builders could succeed in developing a model that would simulate accurately the workings of the economy, and with such a model, if the values of all its fixed parameters could be measured, we would then have an excellent and reliable tool for forming predictions and forecasts of the future state of the economy or of the effects of changes in government policies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".