A comparison between qualitative and quantitative histories: the example of the efficient market hypothesis
Bibliographic record
Abstract
This paper uses the example of the history of the efficient market hypothesis (EMH) and citation analysis in order to investigate some differences between qualitative history and a quantitative history. The history of the EMH provides a telling example of the way quantitative analyses can supply different perspectives on the qualitative history of this hypothesis or complement it. For instance, since the EMH was proposed, several criticisms emerged. In addition, the definition and the scope of this hypothesis have been modified several times. Although the qualitative history of the EMH refers to these criticisms and these alternative definitions and scopes, qualitative tools cannot provide a clear measure of the impact of these criticisms and these modifications among economists. By studying the dissemination of the EMH, its major criticisms, and the answers economists provided, citation analysis sheds a different light on the history of the EMH.
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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 | 0.016 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.007 | 0.022 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".