Investigation Into the Multiple Intelligences of the English Major Postgraduates in a Normal University
Bibliographic record
Abstract
Multiple Intelligences Theory (Gardner, 1983) mainly involves seven kinds of intelligence, which are linguistic intelligence, logical mathematical intelligence, special intelligence, bodily/kinesthetic intelligence, musical intelligence, interpersonal intelligence, intrapersonal intelligence. All the various types of intelligence are respectively independent but interconnected. Various intelligence and intellectual combination result in individuals’ various abilities and ways to think about the problems and to solve the problems. This study is carried out to investigate the overall condition of multiple intelligences of the English major postgraduates in normal university, to find out on which types of intelligences English major postgraduates perform better. A multiple intelligences questionnaire has been administered in order to elicit 133 English major postgraduates’ responses. According to the results, English major postgraduates in normal university perform negatively on multiple intelligences, especially musical intelligence and linguistic intelligence. While among the seven types of intelligences, participants perform best on intrapersonal intelligence. Such result can not only provide inspirations for English postgraduate education, but also deserves both teachers’ and postgraduates’ reflection in the aspect of their teaching or learning styles, the design of teaching activities, course arrangement, etc. as well.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".