JPMorgan Chase London Whale F: Required Securities Disclosures
Bibliographic record
Abstract
On April 13, 2012, JPMorgan Chase (JPM) Chief Financial Officer Douglas Braunstein took part in a conference call to discuss the bank’s first quarter 2012 earnings. Coming just a week after media reports first questioned the risks taken by JPM derivatives trader Bruno Iksil, Braunstein made a series of assertions about the trades. On May 10, JPM finalized its first quarter financial results, which included some disclosures regarding Iksil’s trading that were substantially different from Braunstein’s statements of April 13. At issue is whether the regulatory filings on April 13 and May 10, as well as verbal comments by Braunstein and Chief Executive Officer Jamie Dimon on those dates, were potentially misleading to investors and thus violated relevant securities laws enforced by the Securities and Exchange Commission.
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.282 | 0.081 |
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".