Dodging Dodd-Frank: Excessive Speculation, Commodities Markets, and the Burden of Proof
Bibliographic record
Abstract
Inspired by the wave of regulatory rulemaking, which followed the 2008 financial crisis and the passage of the Dodd-Frank Act, this article examines the efforts of the Commodities Futures Trading Commission to implement one such rule: Rule 76 FR 4752. Born of concerns with the impact of financial speculators on commodities prices, the rule calls for the expanded use of position limits to control “excessive speculation” in US commodities markets. In documenting the political and legal life of this rule from its roots in policy reports through to its suspension by a federal judge, the article explores the place of “evidence” in the rulemaking process. Particular attention is devoted to the growing evidentiary burden placed on financial regulators who are expected to frame market problems in terms of quantitative, price-based forms of harm. In the case of position limits, this has involved statistical analyses of the causal connections between excessive speculation and commodities prices and the use of a single statistical test: Granger causality. By examining the parameters and limitations of this test, the article offers a valuable window into the unique challenges of financial regulation and their roots in questions of knowledge, evidence, and proof.
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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 | 0.026 | 0.115 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.041 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.014 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 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".