Predicting Resilience: A Financial Management Perspective
Bibliographic record
Abstract
Abstract: This study empirically investigates the association between financial management and power plant resilience to hurricanes, addressing a significant gap in the multilevel resilience literature. Utilizing panel data regression analysis of financial metrics from Capital IQ and resilience data from The US Department of Energy reports, the research reveals that return on assets and total revenue are positively correlated with resilience. Conversely, average payable days outstanding, current ratio, inventory turnover, and EBITDA show a negative correlation. These findings suggest that deliberate management of working capital, particularly optimizing inventory for maintenance, is critical for enhancing resilience, as plant availability directly influences revenue. As the first study to operationalize power plant resilience and link it to financial ratios, this research provides original evidence validating sound financial management as a crucial antecedent and dimension of organizational resilience. Keywords: Predictive Resilience, Financial Management, Business Continuity, Accounting Ratios JEL Classification Number: O10, O44
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".