Pembudayaan Berpikir Kritis Dalam Pembelajaran Matematika Di MIM Gonilan
Bibliographic record
Abstract
The aims of this study were (1) to describe how to cultivate critical thinking in the preliminary activities of learning mathematics at MIM Gonilan (2) how to cultivate critical thinking in the core activities of learning mathematics at MIM Gonilan (3) how to cultivate critical thinking in the closing activities of learning mathematics at MIM Gonilan? This research uses descriptive qualitative research with a case study design. The research was conducted at MIM Gonilan Elementary School, with the object of research being the Cultivation of Critical Thinking in learning mathematics. Collecting research data using interview, observation, and documentation methods. The data that has been collected is analyzed in three steps: data condensation, presenting data (data display), and drawing conclusions or verification (conclusion drawing and verification). While the validity of the data with technical and source triangulation. The results of the study show that cultivating critical thinking in mathematics learning at MIM Gonilan uses a problem based learning approach, contextualizes learning material with the daily lives of students, asks questions using HOTS questions, evaluates the questions given, ensures students understand all learning activities to civilize critical thinking at MIM Gonilan.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 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".