Argumentation-Based Learning in Social Studies Teaching
Bibliographic record
Abstract
An argument is the product that is produced as a result of the discussion to support a claim. Argumentation is a reasoning process in which arguments are generated by using claims, data and reasoning components. Argumentation-based learning is an effective approach that can be used to discuss ideas on sociological issues, and is particularly effective in teaching semi-structured problems, such as sociological issues. Social studies are the process of forming a bond based on proofs via social reality and getting dynamic information as a result. The aim of this study is to determine the place of argumentation-based learning in social studies teaching. Whether or not the argumentation-based learning approach is applied in social studies teaching constitutes the research problem. According to the data obtained from this study, it is thought that argumentation-based learning approach can be applied in social studies teaching. In this study, one of the qualitative research methods used in the document analysis method was used.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".