Sociocultural Cultivation of Positive Attitudes Toward Learning: Considering Differences in Learning Ability Between Neanderthals and Modern Humans from Examining Inuit Children’s Learning Process
Bibliographic record
Abstract
To consider the evolutionary basis of modern humans’ learning ability and thereupon build a hypothesis of the key differentiating factors in learning abilities of Neanderthals and modern humans, this study examines the sociocultural backgrounds of Inuit adults’ behavior of teasing children and examines the purpose behind the behavior. First, I introduce a hypothesis to account for the difference in learning ability between Neanderthals and modern humans, which I have proposed on the basis of Tomasello’s model of cumulative cultural evolution and Bateson’s model of learning evolution. I then examine examples of Inuit adults’ teasing of children to understand the characteristics of teasing. Next, I situate their teasing in a sociocultural background, demonstrating that teasing functions as a device for pre-learning, which is the basis for observational learning and creative invention. Finally, using the findings of these analyses, I propose that the most important differentiating factor in learning ability between Neanderthals and modern humans is not in biological ability but in sociality, i.e., the way to collectively generate and actively be involved in sociocultural institutions.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".