Inclusion and Implementation of the Dynamic Dimension in the Process of Strengthening Resilience of Society: the Case of the London Terrorist Attacks of 2005 and 2017
Bibliographic record
Abstract
Introduction.The ecological notion of resilience has been adopted by different Western theoretical approaches in a wide range of scientific fields from economics to political studies.Canadian ecologist С. Holling defines resilience as a measure of the persistence of systems and of their ability to absorb change and disturbance (dynamic part) and still maintain the same relationships between populations or state variable (static part).The aim of this article is to identify the role of the dynamic dimension in strengthening resilience of British society during the period between the two London terrorist attacks of 2005 and 2017.Methods.The methodology of the paper is based on the discourse analysis of the key British documents on public security and the Internet publications immediately after the tragedy.Considering the theoretical aspect, the author distinguishes 'the bouncing back' approach (emphasizing static dimension), 'complexity' (dynamic) and 'adaptive cycles' being a middle ground.Analysis.Analysis of the key British security documents has shown that the government policy on strengthening resilience failed as it was characterized by an excessive predominance of the static element over the dynamic one.However, during the crisis, the London society managed to respond through specific set of adaptation measures such as grassroots self-organization through social networks, strategies of normalization and the phenomenon of habituation.Results.Results of this paper can be summarized in the following points: D. Chandler's concept of 'complexity' has the significant potential for understanding the effectiveness of counterterrorism strategies and ensuring public security.Russia, also facing the threat of international terrorism, should take into account the negative experience of British colleagues and avoid underestimating the preventive dimension of strengthening resilience.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.023 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| 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".