Exploration of Multiple Intelligence by Using Latent Class Model
Bibliographic record
Abstract
In this article we have tried to explore, "multiple intelligence" in the educated youth through questionnaire items by applying latent class models. A questionnaire consists of 50 questions. These questions have constructed in the light of Howard Gardner theory of multiple intelligence to explore "multiple intelligence". A survey was conducted on 399 adult students from different regions of Karachi. For statistical analysis we have selected three sets with seven variables, and one set with 4 variables each with binary response. On these four sets up to three classes latent class models were applied. The Probability of positive response (?iy) in each class were estimated by using E.M algorithm and interpreted the class as on the basis of ?iy values. By assessed goodness of fit latent classes/ groups were identified. Two class (two groups of people) model was found in all four data sets. A group (class) consists of the people who think that they have strong verbal expressions abilities, effectively use language to express himself/herself theoretically and poetically, they have good ability to recognize musical pitches, tones and rhythms, we may call this class as "self competence and self esteem" as "musically talented" as "socialize" (having high interpersonal ability).
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".