Bibliographic record
Abstract
Macroeconomic model builders always face a choice of how to go about building models, although far too many North American model builders are unaware of the options. There are many reasons for the lack of awareness, but they fall into three categories: sociological, historical and methodological. Few graduate students are given any training in how to go about choosing a modeling method. Instead, they are encouraged to follow their teachers’ examples of demonstrated successful modeling. Needless to say, for a decade or more the sub-discipline of the history of economic thought has not been considered an essential part of anyone’s graduate economics training; consequently, there has been little opportunity to become of aware of historical debates about the best or most appropriate model building methods. An obvious sociological reason is that beginning professors need to worry about their careers. Which modeling method is chosen will be the one that helps in obtaining tenure and promotions. Today, too often, the criterion employed for such promotion and tenure decisions is the quantity of publications. So, if one were aware of several alternative modeling methods to choose between, clearly it would be sensible (dare we say rational?) to choose the method that will maximize the number of published papers within the time allotted before the next tenure or promotion decision – or perhaps just the next salary decision. Methodology-oriented critics of the lack of awareness of alternatives point to the methodological issue of the unrealism of the models created – in particular, models that are the easiest to publish are often ones that disregard the unrealism of the assumptions of the model. In other words, critics of the methods practiced in North America say that too often convenience is put before the time-consuming problem of building realistic models.
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 | 0.027 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".