The role of seperation of powers in a legal state
Bibliographic record
Abstract
Artūrs Sņegirevs. Bakalaura darbs par varas dalīšanas lomu tiesiskā valstī. Darba vadītāja: Dr.iur., prof. Sanita Osipova, Rīgā, 2025. Darba mērķis ir izpētīt tiesiskas valsts un varas dalīšanas teoriju ģenēzi, vēsturisko kontekstu, kādā šie konstitucionālie principi izveidojās un kā reizē ar laiku mainās izpratne par pareizo varas dalīšanas modeli un izpratne par tiesisku valsti no 17. gs. beigām līdz 20. gs. otrajai pusei tādu tiesību filozofijas domātāju kā Džona Loka, Šarla Luija de Monteskjē, Džeimsa Medisona, Aleksandra Hamiltona, Leona Digī, Alberta Venna Daisija un Frīdriha Hajeka darbos. Autors darbā secina, ka teorija par tiesisku valsti tika izstrādāta un, attīstoties juridiskajai domai, pilnveidota ar varas dalīšanas teoriju ar vienu vienīgu mērķi – aizsargāt cilvēka cieņu, ierobežojot valsts varu. Eiropas tiesiskajā telpā vēsturiski izveidojās trīs dažādi, bet saturiski līdzīgi tiesiskas valsts modeļi – Rule of law, État de droit, Rechtstaat. Šiem modeļiem ir dažāda pieeja pie varas dalīšanas un atšķirīga nozīme tiesiskajai valstij izvirzāmiem kritērijiem.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.040 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".