Risk in Leadership and Management: Risk-seeking vs. Risk Averse
Bibliographic record
Abstract
The 21st century business environment is full of dangers and risk. At the same time, it is ripe with opportunities and rewards. As organizations continue to pursue opportunities and rewards with vigor, while minimizing unnecessary exposure to risk, leaders find themselves with one of two perspectives on risk: risk-seeking or risk averse. Leaders who are risk-seeking are not moved by the inherent dangers of risk, and understand risk to be a necessary part of the business landscape – and therefore engage risk head-on. Alternatively, leaders who are risk averse understand the implications of risk on the enterprise, and work to minimize, or even avoid risk altogether, or at least to the furthest extent possible. This paper examines risk from a leadership perspective. There are various types of risk that business leaders face, and those will be identified and described herein, along with a discussion of various approaches for addressing risk and how risk is manifested in the business environment. Finally, this paper will assist leaders in determining their own attitudes toward risk using various self-assessment tools.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".