Profession recommendation based on multiple intelligence for high school students
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
One of the problems students often face is the lack of understanding of their interests and talents which will cause confusion in making future study choices and career plans. Career selection is an expression of personality into the world of work followed by identification of certain occupational stereotypes. Eight intelligences according to dr. Howard Gardner is linguistic, logical, mathematical, visual spatial, musical, kinesthetic, interpersonal, intrapersonal, and naturalist. This research purpose is to develop a system that produces information and professional recommendations that are in accordance with multiple intelligences of prospective high school students using a combination of Bayes' theorem and weighted product (WP) method. User’s preference value is calculated using the Bayes Theorem method to give each multiple intelligence value which is a criterion. WP method calculation to find professions that match user preferences. Weight of each criterion needed in WP method is calculated by Rank Order Centroid method. WP calculation will produce a ranking of 3 professions according to the input from the user. From system testing results that compared to results from experts, an accuracy of 67.33% is obtained. Based on the accuracy value, it can be said that the accuracy level system is quite good.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it