Bibliographic record
Abstract
When the Internet reached Russia in the mid-1990s, Russian judicial chiefs actively embraced the idea of having a solid presence of national judiciary on the Web. To judges, having court Web sites would improve public awareness about Russian courts and relieve overloaded court clerks from answering mundane questions about the location of courthouses, hours of work, schedule of hearings, court forms, and so on. However, the chronic underfinancing of Russian courts in the 1990s and the decentralized nature of the Russian judiciary made the creation and the maintenance of the lower courts’ Web sites much more sporadic. Improving public awareness about Russian courts is a priority for Russian judges, who increasingly issue impartial decisions yet at the same time face growing public skepticism about judicial performance (Solomon, 2003, 2004; Trochev, 2006). As the growing number of studies of the information and communication technologies (ICT) in courthouses around the world show, computerized courts can both speed up the administration of justice and strengthen public trust in the judicial system (Bueno, Ribeiro, & Hoeschl, 2003; Dalal, 2005; Fabri & Contini, 2001; Fabri & Langbroek, 2000; Fabri, Jean, Langbroek, & Pauliat, 2005; Langbroek & Fabri, 2004; Oskamp, Lodder, & Apistola, 2004; Valentini, 2003; Malik, 2002). Indeed, as the recent research demonstrates, those who know something about the courts: either about court procedures or about court-ordered public policies, tend to trust the judiciary and to comply with court decisions (Baird, 2001; Gibson, Caldeira., & Baird, 1998; Kritzer & Voelker, 1998; Tyler & Mitchell, 1994; Tyler, Boeckmann, Smith, & Huo, 1997). This article focuses on the Web sites of Russian courts as the virtual gateways in the world of judicial administration (Trochev, 2002) and discusses challenges of adapting Russian court Web sites to the needs of various users of judicial system: judges themselves, law-enforcement agencies, actual litigants, general public and scholars (Toharia, 2003).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".