Olimpíada de Raciocínio Lógico: relatos de uma competição para alunos ingressantes em curso de nível superior
Bibliographic record
Abstract
This paper describes the Logical Thinking Olympiad, an event that aims mainly for freshman students of Bachelor of Information Systems, and has been collaborating on two important points. First it contributes to the disciplines such as algorithms and programming language for a primary analysis of how the student makes use of logic. Second because it facilitates the integration of students of the course itself as well as those from other courses. Resumo. Este artigo descreve a Olimpíada de Raciocínio Lógico, um evento voltado, principalmente, para alunos ingressantes de um curso de Bacharelado em Sistemas de Informação, e que vem colaborando em dois pontos importantes. Primeiro contribui com disciplinas, como Algoritmos e Linguagem de Programação, para uma análise primária de como o aluno faz uso da lógica. Segundo porque facilita a integração entre alunos do próprio curso, bem como destes com alunos de outros cursos.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".