Karakteristik Pemilihan Moda Transportasi Menuju Kampus Universitas Katolik Widya Mandira Kupang
Bibliographic record
Abstract
The study of the characteristics of the selection of transportation modas to campus by Widya Mandira Catholic University Kupang students aims to identify the characteristics of the selection of transportation modas to campus and determine the factors that influence the choice of transportation modas to campus. This study was conducted using a descriptive analysis method on the characteristics of the selection of transportation modas to campus by students and an evaluative method in the form of variable correlation analysis that affects the choice of transportation modas to campus by students of Widya Mandira Catholic University Kupang. The results of this study showed that respondent students who used motorcycles (47.78%), cars (0.00%), follow friends (7.78%), walk (38.89%), ride (0.00%), city transportation (0.00%), and online vehicles (5.56%). Logistic binary regression analysis for factors that influence the choice of transportation moda is known that there are 3 (three) variables that influence the choice of transportation to campus, namely gender (sig. 0.041), distance of residence (sig. 0.034), and health/environmental considerations (sig. 0.027). The conclusion of this study explains that the variables of gender, distance of residence, health/environmental considerations are variables that influence students in choosing modas of transportation to the Widya Mandira Catholic University Kupang campus.
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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 | 0.001 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".