Disjointed modes of building resilience to socio-environmental crises
Bibliographic record
Abstract
Socio-environmental crises are bound to worsen and bring about unprecedented uncertainties regarding their anticipation and management. We studied how public organizations in Finland build resilience to such crises (that is, the capacity needed to deal with and recover from disruptions) and how and why their efforts appear dysfunctional in the face of worsening crises. Our data-driven qualitative analysis draws on interviews with 58 experts in Finnish public organizations in different fields of service provision. We find that resilience is built in three distinct yet interacting modes: Mode 1, as operational action and readiness; Mode 2, as prevention and planning; and Mode 3, as exploring and sketching the unknown. These modes are distinguishable from one another by features such as severity of disruptions, temporal scales of operation, degrees of uncertainty, actionability of measures and attribution of responsibilities, and ways of knowing the future. Based on our analysis of how the modes interact, we diagnose a mutual disconnect between Modes 2 and 3. The demands that the latter imposes on planning and modeling find little grounding in current practices in Finnish public organizations. Conversely, operations in Mode 1 and Mode 2 can be so institutionally locked in that they end up constraining the ability to account for and adapt to any threats beyond tried-and-true methods. It is this disjointedness that, we suggest, permits socio-environmental crises to creep in. We encourage scholars and practitioners to inquire into and identify similar disconnects that can turn aspirations for resilience in organizational practices against themselves. Not only could this elucidate the different kinds of efforts to build resilience in and across governance domains, but it would also help identify whether established practices are able to incorporate uncertainties that can be conjectured but not known for certain.
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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.047 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".