The Current Financial and Economic Crisis: Empirical and Methodological Issues
Bibliographic record
Abstract
In this paper we describe the main causes of recent financial crisis as a result of many theoretical, methodological, and practical shortcomings mostly according to heterodox, but also including some important orthodox economists. At the theoretical level, there are problems concerning teaching and using economic models with overly unrealistic assumptions. In the methodological front, we find the unsuspected shadow of Milton Friedman’s ‘unrealisticism of assumptions’ thesis lurking behind the construction of this kind of models and the widespread neglect of methodological issues. Of course, the most evident shortcomings are at the practical level: (i) huge interests of the participants in the financial markets (banks, central bankers, regulators, rating agencies mortgage brokers, politicians, governments, executives, economists, etc. mainly in the US, Canada and Europe, but also in Japan and the rest of the world), (ii) in an almost completely free financial and economic market, that is, one (almost) without any regulation or supervision, (iii) decision-taking upon some not well regarded qualities, like irresponsibility, ignorance, and inertia; and (iv) difficulties to understand the current crisis as well as some biases directing economic rescues by governments. Following many others, we propose that we take this episode as an opportunity to reflect on, and hopefully redirect, economic theory and practice.
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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 | 0.091 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".