Bibliographic record
Abstract
How can funding of political parties affect the outcome of elections? What impact has the legislation on financing political parties on political system? Is it possible that the government's political parties using this scheme remain in power? I will bring in this work the current theoretical background by leading experts on financing of political parties, as well as a separate chapter on giving patterns, showing signs of functionality and portability to other political systems. Finally, I will analyze trends of legislative funding of political parties in the Czech Republic, the current adjustment and its impact on the Czech political system. In all areas, I will try to identify the basic areas of problems and dilemmas. I will outline possible solutions where appropriate, to which I result of studying the theory, or of my own invention. In conclusion the objective of such analysis is to monitor and verify the following hypotheses. 1. Amendments to legislation are intended to enhance the transparency of the financing of political parties and to prevent corrupt behavior. 2. The funding of political parties tends to strengthen their cartelization, i.e. making access of political parties outside the establishment to high politics difficult. 3. The legislation meets the basic requirements placed on it, its implementation is feasible and its compliance is enforceable. The keywords are: the funding of political parties, (mainland) horizontal contribution, (Canadian) system of tax credits, (German) matching funds, contribution to the work, contribution to the electoral costs, contribution to the mandate, the Constitutional Court.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".