Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it