Evaluation of E-Petition Portals in the World/Türkiye and CİMER in the Context of Governance Principles and Artificial Intelligence/Big Data Perspective
Bibliographic record
Abstract
The Presidential Communication Centre (CİMER) is an electronic government public relations platform that promotes democratic participation by mediating the exercise of the right to petition and right to information in Türkiye. The research problem concerns the lack of a collective petition option in CİMER, the non-publication of petitions, and the lack of AI integration, which limits the effective implementation of governance principles. The aim of this study is to examine the participatory practices of e-petition systems in Türkiye and worldwide, as well as the efficiency-based findings obtained from empirical research on AI and big data technologies, in order to provide governance-focused recommendations to CİMER. In the study, a total of 13 e-petition environments were selected as the sample, including one national government platform each from Europe and North America (Germany, United Kingdom, Russia, Estonia, Canada), two each from India and Türkiye; three international official systems from the European Union; and one unofficial portal (Change.org). The thematic analysis method was used, and the sample was evaluated within the framework of the principles of participation, accountability, transparency, and efficiency. The findings showed that some platforms increased interaction through collective petition and citizen participation tools, that making petition content publicly available strengthened transparency and accountability, and that AI applications contributed to increased efficiency. In this context, recommendations have been developed for CİMER, such as a “public signature system,” the ability to publish petition texts and responses, and the ability to submit petitions at the legislative level. In the context of AI, the following six recommendations were presented: classification; summarization; petition/response text writing; spell check tool; identification of urgent petitions; and chatbots. It was concluded that the proposals could support efficiency by ensuring the accuracy of petitioning processes and preventing duplicate petitions, strengthen transparency and accountability by ensuring that citizens perceive the feedback mechanism as effective, and increase citizen participation by offering opportunities for collective interaction.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| 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".