Legal Mentality of Diasporas: Experience in Theoretical and Methodological Design
Bibliographic record
Abstract
This article presents theoretical and methodical as well as pragmatic basis of the theory of mentality of diasporas and provincial communities which is just taking shape in domestic legal and political knowledge. Great attention is paid, in particular, to legal mentality of diasporas’ representatives manifests itself in a different way in the social space of a multinational state. The authors of this work believe that turning to steady in time mental attitudes common for representatives of different diasporas is necessary first of all for forming an adequate political and legal strategy of the national development, for pursuing relevant legal policies in different Russian regions. The contents and peculiarities of the legal mentality of diasporas and provincial communities should be taken into account in the process of defining priorities of reforming the legal and political system, choosing ways and methods of changes in the legal, political and socioeconomic fields. Besides, thorough research of legal mentality of different diasporas will give a chance to find out the ethnocultural component of dynamics in social processes and to assess them (e. g. in the issues of level of riskogenicity and conflictogenicity), to outline possible results and perspectives. The article considers features of legal mentality of the diasporas of the Far Eastern region of Russia as a specific example.
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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 | 0.031 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.029 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 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".