Bibliographic record
Abstract
The aim of this research is to evaluate the OpenZeka Mini Autonomous Race Car (MARC), Turkey’s first autonomous vehicle competition, within the scope of the education program. The study employed the case study technique, adopting “Responsive” and “Analytical” Program Evaluation Models as the research framework. Initially, competition documents related to the training program were examined. Subsequently, interviews were conducted with trainers and contestants, focusing on their views regarding the alignment of the training with its objectives, its content, the teaching process, and the assessment and evaluation procedures. The findings highlighted several benefits of participating in the MARC competition and completing its training program. These included support for career development, the provision of foundational knowledge necessary for operating autonomous vehicles, and contributions to the national talent pool in artificial intelligence and autonomous technologies. However, the research also identified limitations of the competition program, such as its high difficulty level, intensive training demands, short training period, and crowded environment. Based on these findings, key requirements for future competitions were outlined. The study delves into the sub-components essential for autonomous driving and evaluates the competition specifically from the perspective of its educational program, providing valuable insights for similar initiatives in the future.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".