Bibliographic record
Abstract
Purpose The aim of this paper is to explore the lessons offered by the financial crisis about the appropriate epistemological approaches to apply in the study of human affairs in general, and of the financial markets in particular. Design/methodology/approach The paper applies a qualitative and historical approach invoking debates in the philosophy of social science to dominant themes and concepts in modern quantitative finance. It is argued that underlying the theory and practice of modern quantitative finance is a commitment to an empiricist epistemology modeled on the natural sciences. Findings In the financial crisis, modern social science, with its positivist/quantitative orientation, was put to the test in a way that it had never been before. That it failed this test is one of the chief lessons of the financial crisis. Mathematical techniques are inherently incapable of accounting for human behaviour. The crisis serves to underline that a fundamental divide exists between the natural and human realms. Practical implications While the mathematical‐positivist techniques continue to hold some promise in the study of finance, it has become obvious that this dominant approach needs be enhanced by more qualitative techniques. Originality/value The paper shows that although in popular media outlets the mathematization of finance has been singled out as a cause of the crisis, the broader implications for the analysis of human activity have not yet been probed. Nor, in the wake of the crisis, has there been a systematic, philosophically informed, critique of the positivist‐quantitative orientation buttressing academic research into the financial markets.
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 | 0.031 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.079 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".