Postignuća na numeričkom skrining testu kod dece iz Srbije i Kanade
Bibliographic record
Abstract
The objective of the survey was to verify the hypothesis that children from \nSerbia and children from Canada differ in their results on the Numeracy \nScreener test (NST) that correlates with the arithmetic competences of \nelementary school children. \nWe sampled 105 subjects aged by 8.10 - 9.58, where the group from Serbia \nincluded all 27 pupils of a third-grade class from an elementary school in \nBelgrade and the group from Canada consisted of 78 third-grade pupils \nfrom an elementary school from southern Ontario. By applying the NST test \n(symbolic and non-symbolic subtests), the basic mathematical competences of \nchildren were examined. \nStatistically significant differences were found in favor of the examinees \nfrom Serbia on the symbolic subtest, whereas in the results of the nonsymbolic \nsubtest no differences were found between the examinees. Also, \nwithin the Serbian sample, no differences were found between girls and boys \nof the applied instrument. \nWe recommend that future research strive to validate the NST on a larger, \nand heterogeneous samples of the same-aged children from rural and urban \nmilieus in Serbia, using the following criteria: non-verbal IQ, student’s marks \nfrom all teaching subjects during the current school year, evaluation scales for \nteachers, questionnaires for the parents and the results of the final math test.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".