Strategic allies: Understanding the nexus of business continuity and operational resilience
Bibliographic record
Abstract
Operational resilience and business continuity are closely related, but each embraces different aspects of organisational stability. Business continuity refers to the processes and procedures an organisation puts in place to ensure that critical business functions continue during and after a disruption. It focuses on the ability to maintain or quickly resume essential operations often through preplanned strategies and recovery plans. Operational resilience is a broader concept, encompassing an organisation's ability to adapt and respond to various types of disruption, not only through recovery but also by anticipating, preparing for and mitigating potential impacts. It includes elements of business continuity but also involves a proactive approach to managing risks, building flexibility into operations and maintaining service during disruptions. In summary, while business continuity is a key component of operational resilience, operational resilience itself is a more comprehensive framework that includes preparedness, response, recovery, and adaptation to changes and disruptions. This paper analyses the working of the two functions, highlighting their synergies and differences, and urges risk managers and business owners to realise their combined need for effective contingency planning in an organisation. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".