The limits of resilience: Knowing when to persevere, when to change and when to quit. By MichaelUngar (1st edition) (2024, Sutherland House, Toronto), 256pp, CAD 19.95, ISBN 978‐1‐990823‐56‐5.
Bibliographic record
Abstract
The 2023 collapse of Wilko, a prominent UK retail chain, exemplifies the 'resilience paradox' in corporate restructuring-a compelling and counterintuitive dynamic within systems, organisations and societies: The very mechanisms that bolster short-term resilience can inadvertently sow the seeds of long-term vulnerability.To solve financial challenges, Wilko secured a £40 million loan from Hilco Capital in early 2023 and implemented cost-cutting measures, including up to 400 job cuts. 1 These actions provided short-term stability but led to overreliance on external financing and reduced operational flexibility.Consequently, Wilko entered administration in August 2023, resulting in the closure of all 400 stores and the loss of over 12,000 jobs. 2 This case highlights how strategies aimed at immediate resilience can inadvertently increase medium and long-term vulnerabilities, underscoring the complex balance between short-term recovery efforts and long-term sustainability.3 Resilience has long been celebrated as the hallmark of human strength and adaptability.4 Michael Ungar's The Limits of Resilience does not take the Wilko case but challenges this simplistic valorisation by revealing its inherent complexities and paradoxes with other examples.As Ungar argues, resilience is not the panacea; it is often portrayed to be; instead, it is a process 1 Sarah Butler, 'Wilko secures £40 m funding from Hilco as it faces cash squeeze' (The Guardian, 4 January 2023). 2 Mark Sweney, 'All 400 Wilko shops to close with loss of more than 12,000 jobs' (The Guardian, 11 September 2023).3 Jane Ingram et al., 'Post-disaster recovery dilemmas: challenges in balancing short-term and long-term needs for vulnerability reduction' (2006) 9(7-8) Environmental Science & Policy 607-613.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.048 | 0.029 |
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".