Comparison of Multiple Intelligence Areas of Hearing and Visually Impaired Individuals in Terms of Age, Gender and State of Doing Sport
Bibliographic record
Abstract
The purpose of the study is to review the multiple intelligence domains of the individuals belonging to two different disability groups (hearing impaired and visually impaired). The study comprises of 63 adults in total, 34 hearing impaired and 29 visually impaired. In order to obtain data, “Multiple Intelligence Inventory” developed by Gülşen (2015) was applied to the respondents as well as the descriptive questions. In the study, t test and Mann Whitney U, and Kruskal Wallis and One Way ANOVA were used. When the multiple intelligence score averages were compared according to the gender variable, the interpersonal intelligence score averages of the females were found to be significantly different than that of the males (p<0.05). It was determined that the naturalistic intelligence score averages of the individuals at the age of 35 or above were significantly higher when the score averages of multiple intelligence domains were compared in terms of age variable and the bodily – kinesthetic intelligence of the individuals doing sports was seen to be significantly higher than those who did not do any sports (p<0.05). When the average scores of multiple intelligence domains were compared in regard to the state of disability variable, the mathematical-logical intelligence of the hearing impaired was found to be significantly higher than the visually impaired (p<0.05). The visual-spatial intelligence of the hearing impaired was observed to be significantly higher than the visually impaired (p<0.05). The musical intelligence of the visually impaired was found to be significantly higher than the hearing impaired (p<0.05). The intelligence score averages of the visually impaired were determined to be significantly higher than the hearing impaired for the interpersonal intelligence domain (p<0.05). It was found that the intrapersonal intelligence of the hearing impaired was higher than the visually impaired (p<0.05). The less developed intelligence domains of the impaired can be worked actively and improved by making regulations in a way that the impaired can involve more in the social life and by allowing them to be exposed to environmental warnings more.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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".