Can we measure cognitive constructs consistently within and across cultures? Evidence from a test battery in Bangladesh, Ghana, and Tanzania
Bibliographic record
Abstract
We developed a test battery for use among children in Bangladesh, Ghana, and Tanzania, assessing general intelligence, executive functioning, and school achievement. The instruments were drawn from previously published materials and tests. The instruments were adapted and translated in a systematic way to meet the needs of the three assessment contexts. The instruments were administered by a total of 43 trained assessors to 786 children in Bangladesh, Ghana, and Tanzania with a mean age of about 13 years (range: 7-18 years). The battery provides a psychometrically solid basis for evaluating intervention studies in multiple settings. Within-group variation was adequate in each group. The expected positive correlations between test performance and age were found and reliability indices yielded adequate values. A confirmatory factor analysis (not including the literacy and numeracy tests) showed a good fit for a model, merging the intelligence and executive tests in a single factor labeled general intelligence. Measurement weights invariance was found, supporting conceptual equivalence across the three country groups, but not supporting full score comparability across the three countries.
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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 | 0.027 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".