WHY CAN ORGANIZATIONAL RESILIENCE NOT BE MEASURED?
Bibliographic record
Abstract
Our aim is to justify why organizational resilience cannot be measured in an ex-ante way and the consequences we can draw from it. To achieve this goal, we examine the relations between different approaches to organizational resilience and the tight interrelation between organizational resilience and organizational and dynamic capabilities. We argue that most proposals about organizational resilience conceptualization, and the metrics derived from them, are closely related. They represent the same core concepts, facts, and relations. Additionally, far from there being no consensus about organizational resilience, researchers are presenting the same ideas with different terms. This implies that there are no better or worse definitions or conceptualizations for organizational resilience, but models are more or less suitable depending on the approach to be established. We agree with the proposal that organizational resilience is a dynamic capability and, as such, it should be studied and considered. This review led us to conclude that because organizational resilience is a dynamic process, it cannot be measured or estimated in an ex-ante way. The fact that organizational resilience cannot be measured brings us to the question of how organizations can address organizational resilience improvement, evaluate their progress, and the tools they can use.
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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 | 0.044 | 0.165 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.009 | 0.033 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".