A Review of the Multiple-Intelligence Domains of Physically Handicapped Individuals in Terms of Certain Demographic Attributes
Bibliographic record
Abstract
The purpose of this study is to review the multiple intelligence domains of physically handicapped individuals with regard to certain variables. The population of the study consists of 40 physically handicapped adults in total, 13 females and 27 males, between the ages of 16 to 58 with an age average of 30.80 ± 9.78 who were members of Association of the Physically Handicapped and who participated into the study voluntarily in Samsun province, in 2017. In addition to the descriptive questions about age, gender, educational background and whether they did sports or not, “Multiple Intelligence Inventory” developed by Gülşen (2015) was applied to the respondents in order to obtain data. In the study, t test and Mann Whitney U test for the paired comparisons, and Kruskal Wallis and One Way ANOVA test for the triple comparisons and above were used to compare multiple intelligence theory score averages of the physically handicapped individuals in terms of the variables of gender, age, educational background and whether they do sports or not. Level of significance was taken as (p < 0.05). It was found that there was a significant difference between the logical/mathematical intelligence domain score averages of female and male participants (p < 0.05). It was seen that the logical/mathematical intelligence domain of males was more dominant than that of females. A statistically significant difference was observed at the Visual/Spatial and Musical/Rhythmic Intelligence domains with regard to age group variable (p < 0.05). In addition, when the multiple intelligence score averages were compared according to whether individuals do sports or not, the logical/mathematical intelligence domain of those doing sports was significantly higher than that of those who did not do sports (p < 0.05). The results can be interpreted as that doing sports may develop logical-mathematical intelligence of individuals. The study also shows that none of the fields of intelligence differs statistically in terms of the variable of educational status.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".