A compreensão de textos de problemas matemáticos: aspetos metalinguísticos e metacognitivos
Bibliographic record
Abstract
Apresentamos uma série de exercícios destinados a desenvolver habilidades ou capacidades metalinguísticas aplicadas a textos ou problemas matemáticos. São uma série de problemas de matemática normalmente utilizados no final do 1º ciclo, podendo ser utilizados, mesmo, em outros ciclos de ensino. Os exercícios compartilham importantes pontos comuns em objetivos e modalidades: 1) sensibilizar professores e alunos para a forma como conceitos e perguntas são formuladas, 2) os alunos praticam uma série de habilidades metalinguísticas, nomeadamente, metasemânticas, metagramáticas e metapragmáticas, 3) os exercícios são sugeridos para serem praticados enquanto atividades de sala de aula, sob a orientação de professores.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Set of classroom exercises for metalinguistic and metacognitive comprehension of mathematics word problems; 'meta' here is metalinguistic, not metaresearch, and the object is school pedagogy.
The work presents educational exercises for understanding mathematical problem texts, not research practice.
Pedagogical exercises for math-problem text comprehension; education practice, not research systems.
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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".