WHY CAN ORGANIZATIONAL RESILIENCE NOT BE MEASURED?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it