Bibliographic record
Abstract
Since Nassim Taleb coined black swan as an event that occurred as a complete surprise for everybody, the metaphor of the black swan has been applied to a much wider variety of events. Black swan events now comprise events that are a surprise for some but not for others, events that have a low likelihood, events that were not believed to be possible but still proved to be possible, events that were dismissed as being too improbable to worry about but happened anyway. For a decision maker the black swan problem is choosing where to put effort to prevent, or mitigate events for which there are warnings, or for which the possibility has been put forward. Does the fact that there are thousands of books written about fire breathing dragons warrant the development of an Anti-Dragon Defense Shield? The black swan may have been a surprise for Willem de Vlamingh in 1697, it was not a surprise for the inhabitants of Australia, for which the appearance of tall white humans was their "black swan event". In this paper we explore the options available to decision makers when confronted with the various sorts of swan (or dragon) events.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".