Experiences of experts in intelligence measurement of South African school learners
Bibliographic record
Abstract
This qualitative research study emerged from the essential need for reliable and valid intelligence test instruments for South African school learners, who are characterised as a diverse population with their variety in culture, ethnicity, and language, as well as having unequal socio-economic and educational backgrounds. The aim of this research study was to use a qualitative interpretive description research design to explore and describe the experiences of both experts in intelligence test development and/or adaptation as well as psychologists and psychometrists who have administered intelligence tests to South African school learners in various contexts. Twelve psychologists and/or psychometrists were interviewed, of which six were also experts in test development and/or adaptation, which yielded four themes after thematic analysis, namely, utilised intelligence measurements in the current South African school learner context are less relevant; the South African education system is a major issue specifically within lower socio-economic status (SES) contexts; it does not seem feasible to design or adapt suitable intelligence measures that are valid and reliable in the current South African school learner context; and key informants' recommendations from their experiences. Contribution: This research study contributes to the understanding of the measurement of intelligence of South African school learners in diverse contexts. Findings of this research study can guide the strategic process to design an intelligence instrument suitable for a South African population of school learners, informing fair assessment practices for multiethnic equalisation.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".