Forecast Evaluation of the Social and Political Tensions Potential for the Proactive Countermeasures against Extremism
Bibliographic record
Abstract
In case of high-level social and political instability in the region, there is the need for taking countermeasures even when only weak signals of tension escalation have been detected. Under these circumstances, the implementation of the proactive countermeasures against extremism is methodologically justified. This allows reducing the degree of social and political tensions for a long term, preventing the formation of tension centres and its support and initialization environments. It has been shown, that the scientific basis for the proactive countermeasures against extremism is the projective methods of generating the initial data, the intellectually, psychologically relevant methods of initial data interpretation, the methods of group theoretical analysis and multiagent modelling, determining the situation development forecasting way. The proposed structure of the intelligent module, which consists of the methods and models of the social and political tension forecasting evaluation, being the scientific basis for the proactive countermeasures against extremism, has been described. Its core includes a new procedure of monitoring the group political attitudes by measuring its affective and cognitive components, the extended psychosemantic phenomenological model (based on more complete integration of the social psychology approaches, the theory of measuring the social processes, the catastrophe theory, etc.), as well as a fundamentally new integrated simulation model of the group and individual response based on the multiagent modelling paradigm. The results of the procedure testing in 2011-2013 while studying the group political attitudes among the students of Rostov-on-Don, Vladikavkaz and Grozny are noted. The opportunities for the development and applicability of the procedure for the direct application in the actual practice of the regional authorities to reduce the degree of the social tensions (the people's protest) and neutralization of the current conflict factors in these territorial communities, namely, the implementation of the proactive countermeasures against extremism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".