The Swiss Black Swan Bad Scenario: Is Switzerland Another Casualty of the Eurozone Crisis?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Financial disasters to hedge funds, bank trading departments and individual speculative traders and investors seem to always occur because of non-diversification in all possible scenarios, being overbet and being hit by a bad scenario. Black swans are the worst type of bad scenario: unexpected and extreme. The Swiss National Bank decision on 15 January 2015 to abandon the 1.20 peg against the Euro was a tremendous blow for many Swiss exporters, but also Swiss and international investors, hedge funds, global macro funds, banks, as well as the Swiss central bank. In this paper, we discuss the causes for this action, the money losers and the few winners, what it means for Switzerland, Europe and the rest of the world, what kinds of trades were lost and how they have been prevented.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it