When do we talk about when we talk about economics?
Bibliographic record
Abstract
Everywhere we look there are “economic indicators.” We talk about the jobless rate and the national debt. We learn about the first quarter and evaluate movies by how much they earn on opening weekend. In the end, life insurance companies determine our “worth.” Does any of this make sense? On the next episode of WHY?, we’ll talk with economic historian Deirdre McCloskey about what these figures tell us and what they leave out. We’ll ask where the human experience is in the midst of all these numbers and investigate economic assumptions that claim human beings are self-interested, and that happiness or desires can be quantified. We’ll even ask whether economics is, itself, a science that leads to objective information. Deirdre McCloskey is a Distinguished Professor of Economics, History, English, and Communication at the University of Illinois at Chicago. She is also a Professor of Economic History, Gothenburg University in Sweden. She is interested in the rhetoric of economics and wider literary matters, such as literary and social theory. Her main project for is writing a six-volume series on “The Bourgeois Era.” The first two volumes The Bourgeois Virtues, Ethics for An Age of Commerce and Bourgeoisie Dignity: Why Economics Can’t Explain the Modern World, have already been published. Deirdre describes herself as is a free-market economist and explains that her project is a defense of capitalism that is fair to both the right and the left. She is the author of numerous other books other than her six-volume project. Her webpage and examples of her work can be found here.
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 | 0.014 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.008 | 0.032 |
| Scholarly communication | 0.025 | 0.035 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.010 | 0.031 |
| Insufficient payload (model declined to judge) | 0.015 | 0.011 |
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".