Bibliographic record
Abstract
The current discussion about a “risk culture” in financial services was triggered by the recent series of financial crises. The last decade saw a long list of hubris, misconduct and criminal activities by human beings on a single or even a collective basis in banks, in the industry or in the whole economy. As a counter-reaction, financial authorities called for a guidance by a “new” risk culture in financial institutions based on a set of abstract, formal, and normative governance processes. While traditional risk research in economics and in banking was focused on the statistical aspects of risk as the probability of loss multiplied by the amount of loss, culture is a paraphrase for the behavior in collectives and dynamics of organization found in human societies. Therefore, a “risk culture” should link the normative concepts of risk with the positive “real-world” decision-making in financial services. This paper will describe a novel view on “risk culture” from the perspective of human beings interacting in dynamical and intertemporal commercial relations. In this context “risk” is perceived by economic agents ex−ante as the consequence of the time lag between the present and the uncertain future development (compared to a probability distribution calculated by observers ex−post). For all those individual decisions—to be made under uncertainty—future “risk” includes the so-called “normal accidents”, i.e., failures that will happen at some uncertain point in time but are inevitable, and the only questions are when failure will happen and how to maintain function in the first line of defense. Finally, the shift from an abstract definition of “risk” as a probability distribution to a role model of “honorable merchants” as a benchmark for significant individual decision-making with individual responsibilities for the uncertain future outcome provides a new framework to discuss the responsibilities in the financial industry.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".