Cognitive Correlates of Different Academic Subjects in School Setting
Bibliographic record
Abstract
This is an empirical study which examined the relevance of intelligence tests (verbal and non-verbal) in differentacademic subjects. A sample of 200 students (100 males and 100 females) of Eleventh class from differentschools of Shimla district in Himachal Pradesh (India) were tested on Standard Progressive Matrices (SPM) andGeneral Mental Ability Test (GMAT) together with their scores on different subjects. A multiple regressionanalysis revealed an interesting pattern of relationship. SPM has been found to be the best correlate ofMathematics and Science subjects contributing 53% and 58% of variance in males' sample and 32% and 36% ofvariance in females' sample. Whereas, GMAT, correlated best with languages and social science subjectsaccounting for 28% to 44% in males' sample and 28% to 56% in females' sample.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".