Bibliographic record
Abstract
Purpose The increasing prevalence of enterprise risk coupled with global competition requires a strategic link to the way forward through measured outcomes determined in a collaborative and visual manner. Design/methodology/approach The model was conceptualized and validated through strategic management deficiencies encountered in venture ownership, management consulting and teaching practice and was augmented with extensive stakeholder input and existing scholarly research. Findings Managing risk is a complex and somewhat dark task that can happen everywhere or nowhere within a firm and requires mitigating strategies developed with stakeholder consensus. In practice with three distinct audiences of entrepreneurs, senior managers and capstone student strategists, the model does facilitate linked risk and opportunity identification as well as the quantification and/or qualification of variables. The process is as impactful as the outcomes when individual risk “appetites” and a collective risk culture emerge. Practical implications Innovative organizations must regularly scan their external environment and assess internal resources and capabilities in a structured and actionable manner while understanding their risk culture. Valuable outcomes emerge from facilitating strategic risk management discussions, particularly in growing and informal organizations. Risk communications with stakeholders are complex and increasingly important. Originality/value Opportunity mapping offers a novel process to identify prioritized strategies that may be otherwise determined intuitively with limited input or absent altogether. The process facilitates actionable outcomes as a supplement to the known practice of risk assessment while contributing to a competitive advantage.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".