Bibliographic record
Abstract
Until macroeconomic theory rebuffs the nature of finance, which is leverage (debt claims and credit instruments above current GDP output), shadow-banking will continue to lure capital into the financial sector, lower institutional banking interest rates, and de-incentivize commercial lending from the real sector, even at competitive risk-adjusted rates. This institutionalized misallocation of credit undermines the tidy neoclassical “circular flow” apparatus where savings and earnings are allegedly pooled and then recycled through financial intermediaries into dynamic investment.Despite the mathematical complexity of DSGE models, the last financial crisis and its aftermath exposed the models’ inadequacy for forecasting or even fully capturing economic reality. With no dynamic function for money, incorporating credit into the theoretical mindset of mainstream economics, including both neoliberal and (Post)-Keynesian traditions, has proven as yet unattainable. Holding fast to a (barter-like) Walrasian worldview, wherein the neutrality of money in the long-run, has meant debt does not exist and credit aggregates are not considered due to an a-historical mis-conceptualization of money/credit. As long as money, credit and debt are not accorded special roles, a bloated financial sector may well contribute to the suboptimal allocation of talents. This Article shows how by design, DSGE models do not recognize either credit’s beneficent growth properties, or credit’s aptitude to precipitate crisis by augmenting, largely non-GDP financial income, while in the long-run simultaneously reducing earned income (GDP recognized).
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.020 |
| Scholarly communication | 0.012 | 0.023 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 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".