Bridging gaps by building walls: Improving social accountability of political parties
Bibliographic record
Abstract
Regulations upon political parties is a discussion brewing from the popularisation of representative democracies, stemming from the core tenets of transparency and accountability. Scholars from the Global North and South alike have argued that regulations enhance the democratic process. Yet, the manifestation of years of expert analysis is still lagging behind. This is explicitly true in India, following decades of legislative and judicial efforts to ensure party regulations, foiled by an extensive lack of robust policies and independent institutions. Through this paper, we make the case for expanding the political regulatory regime in India with the argument that political parties play a role similar to corporations in the economic sphere and thus, require a similar degree of regulation. Furthermore, we explore the international precedents set by states with expansive regulatory frameworks and years of institutional capacity building. For this, we will focus on Italian and German regulations as a core case study. Lastly, by refining these resolutions into actionable recommendations fitting within the Indian context, we present our core recommendations on the core question of improving social accountability of political parties.
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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 | 0.034 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 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".