Multiple Intelligences Profiles of Junior Secondary School Students in Indonesia
Bibliographic record
Abstract
This study aimed to investigate the Multiple Intelligences profiles of the students at junior secondary school in Makassar. The Multiple Intelligences Inventory was used to identify the dominant intelligence among the students. The sample of this research was 302 junior secondary schools students in Makassar Indonesia who willing to participated in this study. Descriptive and inferential statistics were used to investigate the students’ MI profiles. The results of this study showed that all intelligences were possessed by the students either in strong, moderate, or weak category. Existential intelligence became the strongest intelligence among the nine types of multiple intelligences. Moreover, other types of multiple intelligences in strong category were interpersonal intelligence and verbal-linguistic intelligence. They were the second and the third intelligence of the strongest intelligences. The other types were in moderate category, were intrapersonal intelligence, musical intelligence, visual-spatial intelligence, logical mathematic intelligence, bodily-kinesthetic intelligence, and naturalist intelligence. In terms of gender, the study revealed, male students significantly possessed stronger in logical-mathematic intelligence, bodily-kinesthetic intelligence, and intrapersonal intelligence, Meanwhile, Female students were significantly stronger in musical intelligence, interpersonal intelligence, and existential intelligence. The results also showed that there was no significant difference between male students and female students in verbal linguistic intelligence, visual-spatial intelligence, and naturalist intelligence.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".