Is it a Small World After All? Investigating the Theoretical Structure of Working Memory Cross-Nationally
Bibliographic record
Abstract
Abstract To our knowledge, this is one of the first studies to test different theoretical models of working memory in childhood based on a computerized assessment. We tested this across several countries: Argentina, Brazil, Canada, Italy, and uk . The present study addressed the wider macro-cultural context and how this impacts working memory. We used two economic indices ( gdp and ppp ) to characterize the participating countries and ranked the countries based on the Global Index of Cognitive Skills and Educational Attainment. Children between 5 and 10 years completed the same set of short-term and working memory tests. There were two main findings. First, there was a similar pattern in verbal working memory across countries, which suggests that this skill may be relatively consistent across different cultural groups. In contrast, the pattern for visuo-spatial working memory was different across countries, which may explained by cultural differences and educational rankings of the countries. The second main finding was that both a domain-general model (3-factor) and a domain-specific model (4-factor) provided a reasonably good fit with the data, there was the high relationship between the verbal and visuo-spatial working memory constructs across the countries in the latter model. Thus, it may be a more parsimonious choice to rely on a three-factor model. The data also suggest culture-similar patterns in a computerized assessment of working memory.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".