Assessing and Re-setting Culture in Enterprise Risk Management
Bibliographic record
Abstract
Among the tenets of enterprise risk management (ERM) is the need to instill a risk-aware culture throughout the firm. Yet, how to actually interpret and change organizational culture is generally missing from the ERM literature. Prior surveys found risk managers lacked useful information about organizational culture and cultural change to implement a “risk aware culture.” Our survey of risk managers found this gap persists. The disciplines of organizational studies, business anthropology and sociology provide guidance on organizational culture, which involves identifying and interpreting the embedded assumptions, values, myths, artifacts, rituals, and stories that communicate and perpetuate a culture. The risk manager can use this knowledge to apply change to the culture. Changing behavior without changing culture may simply result in compliance without adoption. This article seeks to bridge the studies of organizational culture and change to the risk manager.
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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 | 0.035 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".