ELECTIONS WITH BLOCKCHAIN TECHNOLOGY: CAN ELECTRONIC VOTING PUT AN END TO THE DEBATES IN TÜRKİYE?
Bibliographic record
Abstract
Elections in Turkey have a long-standing tradition, with citizens going to the polls being considered a hallmark of democratic participation. However, issues related to security, transparency and legitimacy have eroded public trust in the electoral process, especially following events such as the use of unstamped ballots in the 2017 Constitutional Referendum and the annulment of the 2019 Istanbul elections. These concerns are not unique to Turkey; similar issues exist globally.Around the world, digital solutions are being explored to address these problems. For instance, countries like Estonia, Switzerland and South Korea have integrated blockchain technology into their election processes. In countries such as the United States and Canada, blockchain-based projects have been developed to facilitate voting for citizens living abroad. The transparent, secure and decentralized structure offered by blockchain holds great potential in preventing vote manipulation and ensuring the reliability of election processes.In Turkey, although SECSIS namely Election Information System digitally manages voter registrations and address information, it does not play a direct role in the voting process. It is important to note the distinction between e-elections and e-voting; e-elections encompass the entire process, while e-voting only involves the act of casting votes. The widespread adoption and acceptance of electronic signatures (e-signatures) also took time, with pilot programs addressing many of the concerns. Similarly, blockchain-supported electronic voting systems have not yet been implemented in Turkey, but it is recommended that this technology be integrated, tested and piloted. By strengthening legal frameworks, enhancing cybersecurity measures and increasing public acceptance, it is believed that blockchain technology could contribute to election security and transparency in Turkey.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".