Stability of Spatial Risk Integration in the Long Term
Purpose: to demonstrate that a viable scientific paper can be written without the need for any meaningful intellectual input whatsoever.
Methodology: one hundred common social science terms were placed in alphabetical order and numbered consecutively. Random number sequences were used to reorder them. New prose was written in order to join up the terms into comprehensible sentences.
Results: a readable and apparently profound scientific paper was written that appears to throw light onto obscure areas of social science thinking and produces the comfortable illusion that useful work has been done.
Key words: Nonsense, Twaddle, Random numbers, Jargon, Social science terminology, Academic blather.
It has long been recognised that stability is one of the key factors in spatial risk integration in the long term. The development of a methodology of prediction allows a scientific approach to the identification of cycles that enable the system to be characterised in terms of information that will provide a technocratic perspective on indicators of civil phenomena. The adoption of a hierarchical set of objectives enables release factors to be identified for instances in which the flux of information is subject to degradation of flows.
Advanced methods allow a trade-off that facilitates coping with dynamic feedback. Optimal multidimensional parameters create conditions for innovation that can be applied to infrastructure and that permit regionalisation to be accomplished. This can be followed by a process of recombination. Guidance for this must take full account of post-modern fragility associated with the hazards in question. Participatory tools available to carry out these tasks require adaptation to the impact of different trajectories.
The scenarios associated with susceptibility necessitate a formulation that involves monitoring hazardous elements. Their management requires a degree of transformable capacity which must take account of factors that include sustainable non-linear domains. This requires considerable awareness of the situations involved, which in turn necessitates review of dose relationships. The social assessment of communities can be accomplished by using a toolbox of institutional attributes subject to implementation as a data base that highlights dependent linkages in the analysis of case-studies.
Societal elements include multiple resilience factors that emphasise the transitions involved in learning from exposure to scale-dependent domains. However, a cutting-edge approach requires variable linkages with a selection of different partners, whose inertia is a function of error curves that different actors regard in terms of thresholds between threat and strategy. Scaling the policy response leads to a state of hysteresis that is exacerbated, moreover, by perturbations in society.
The normative assumption for different regimes allows an option to be considered for stakeholder involvement in antecedent events of a magnitude and complexity that are critical to processes of governance. Normalisation of these processes enables one to focus on vulnerability and thus widen the panorama of different objective assessments that are available to stakeholders.