Bibliographic record
Abstract
In this chapter, we draw on Gibson's (1979) description of affordances to consider cultural differences in motivation and learning. We develop the argument that affordances are at the heart of cultural differences. We address the way culture influences both what and how people learn from the affordances that are available to them in their physical and social environments. Brain processes of neural plasticity and psychological learning mechanisms of repetition and connection drawn from the Unified Learning Model (Shell et al., 2010) are used to explain how our brain and memory store knowledge of affordances, as well as the actions needed to take advantage of these affordances. We then discuss the way attention sits at the intersection of motivation and learning, as well as how motivated attention leads to individual and cultural differences in knowledge and use of affordances, both implicitly and volitionally. Finally, the emergence of cultural differences in attention, learning, knowing, and motivation are discussed, with an emphasis on the impact of culture on learning in school.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".