Role of Logic and Mentality as the Basics of Wittgenstein’s Picture Theory of Language and Extracting Educational Principles and Methods according to this Theory
Bibliographic record
Abstract
The present paper attempts to recognize principles and methods of education based on Wittgenstein’s picture theory of language. This qualitative research utilized inferential analytical approach to review the related literature and extracted a set of principles and methods from his theory on picture language. Findings revealed that Wittgenstein believed in language as a picture of the real and assumed that the real is reflected in language. He believed that language and mentality are the same and language demonstrates a full picture of mentality. Besides, the world and the language possess a logical structure and this logic rules the world and the language. Later on, his picture theory of language, logic and mentality were used to extract and introduce principles for education as listed here: the reasonability principle, mind involvement principle, matching principle, reasoning principle, creativity principle and formation of mind, comprehensibility principle, liberal thinking principle, and the principle of considering individual differences. Thus, applying the method of concept comprehension, problem oriented method, heuristic method, brainstorming method and finally interactive methods like Socratic question and answer and group discussion method.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.031 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".