Bibliographic record
Abstract
This presentation was occasioned by the author's distinction with a Diploma Honoris Causa, for his past work, by the Romanian Academy. It outlines a synthesis of his epistemological approach to economics as a science, most particularly the differences between this science and the sciences of nature. The following differences are discussed in detail: [1] The object of physics, chemistry, biology etc. is essentially the same while the science advances. Instead the object of economics changes while the science tries to keep up with it. [2] The relationship between the science and its object; the agents of the economy may take cognizance and adapt their behaviour according to what science says, while atoms, molecules, cells, stars etc. are "indifferent". [3] The sciences of nature are the basis for the creation of a world parallel to that of nature, i.e. the "world" of the laboratory and technology. Economics always applies to the same world which it studies. [4] The economist is himself one of the many agents of the economy so that his experience can be a source of knowledge. Many comparative examples are given, including some concerning the author's past research jobs in Romania, and more recently in Canada. The theory of games is quoted as illustrating the relationship between the economist and the agents of the economy. It is a version of game theory first discussed by this author and outlined in several of his mentioned works.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 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".