Resilience, Dynamism and Sustainable Development: Adaptive Organisational Capability Through Learning in Recurrent Crises
Bibliographic record
Abstract
Abstract Being resilient is often equated with the capability to return to a state of normalcy after individuals and organisations face unprecedented challenges. This chapter questions the notion of ‘normalcy’ in complex and ongoing turbulence as experienced variously in diverse cultural and sectoral contexts. In theorising organisational resilience and associated transformation, it draws on insights provided by a microfinance institution (MFI) operating in the Philippines. The chapter details its efforts to transform business in light of experience gained in frequent and overlapping emergency conditions (including COVID-19) to create a new level of resilience in its clients and itself. For clients, the goal is often to self-manage loss associated with socio-economic development and for the organisation, to stabilise and cordon the investment needed to support clients survive and move on from the relatively constant adverse impacts of disasters. Published accounts of such experience and insights provided by board members and the President illustrate the nature of transformational resiliency strategies planned, including changes to the business model around provision of micro-insurance services and strategic adaptation of digital services aligned with the organisation's mission. A model of ‘practical resiliency in emergency conditions’ details the culture of resiliency adopted, demonstrating how stakeholders gain confidence and opportunity to practice resilient behaviours in emergency contexts. It highlights the significance of cultural consistency across purpose, values and capability to create an adequate level of trust and certainty across stakeholders to support transformational resiliency behaviours in shifting and dynamic ecosystems.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".