Risk or Opportunity? Firm Strategies in the Face of Policy Uncertainty and Disruptions
Bibliographic record
Abstract
Businesses increasingly deal with a complex (geo)political environment, resulting in high uncertainty and ambiguity. Policy uncertainty is generally seen as a barrier to clear decision-making, investment, and production, yet it also presents opportunities for businesses to navigate and potentially exploit this uncertain environment. This raises the question of whether policy uncertainty is a risk, an opportunity, or both. We delve into the key question of what type of firms are more vulnerable to, protected from, or capable of leveraging these uncertainties. In doing so, we first focus on clarifying the concept of uncertainty at different levels (e.g., global, national, industry, firm) and the different types and sources of uncertainty (e.g. financial, regulatory, geopolitical, corporate wrongdoing). In this symposium, the panel will provide insights into different types of uncertainty and their nuances, encompassing domestic and international perspectives. Further, we will discuss methodologies, data, and innovative research designs, setting the stage for addressing the critical questions that will shape the next decade of research on policy uncertainty and firm strategies.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".