Bibliographic record
Abstract
Economics explains human prosperity as arising predominantly from a process of creative destruction: Successions of innovators create new wealth by conceiving and developing new higher productivity technologies that destroy, partially or completely, the wealth built by their predecessor technologies. Because higher productivity is, by definition, the production of more or more valued outputs from less or less costly inputs, creative destruction increases wealth over the long run. Economic models, in hopeful emulation of the natural sciences, are built from quantifiable probabilities and outcomes. However, new technologies are new creations of human minds, previously unconceived, let alone assigned probability distributions over well-defined outcomes. Economics must be more ambitious. Economics seeks to explain not merely decision-making in an expanding space of conceivable probabilities and outcomes, but decision making that causes that expansion. Behavioral economics reveals that humans rarely think in terms of quantitative probabilities and outcomes, but typically use narrative decision-making. Confronted with a problem, humans formulate a response by recalling and recombining narratives – actual or learned memories of problems, responses and outcomes, each triad with an emotional weight. New narratives arising as recombinations of existing narratives, and economically selected for higher productivity, potentially explains combinatorial economic growth.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".