REGULATION OF FINANCING OF ONLINE ELECTION CAMPAIGNS: INTERNATIONAL PRACTICE AND ELECTIONS IN KAZAKHSTAN
Bibliographic record
Abstract
This study attempts to identify the extent to which current legislation on electoral campaigning is effective in addressing digital forms of campaigning. Ensuring that all forms of campaigning by political parties and candidates adheres to principles of transparency and equality is instrumental in preserving the integrity of elections. The data used for this research includes the analysis of legislation from the European Union, the United Kingdom, the United States and Canada. Research also uses data on campaign advertising regulation introduced in the run-up to the 2023 parliamentary elections in Kazakhstan. Evidence suggests that legislation has been slow to respond to ever evolving forms of digital campaigning. In the context of Kazakhstan, while certain improvements have been introduced in legislation, it mostly relates to extending existing norms aimed at traditional campaign tools (TV and print media). As such, it is insufficient in addressing some of the bigger concerns related to financial accountability during elections. Given the latest developments in technology as it is used in political context, more measures are required for proper regulation.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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; a candidate call from one teacher head, 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".