Related factors between academic performance and transportation of students to the University "UNIANDES"
Bibliographic record
Abstract
Introduction: transport is known as a set of processes whose purpose is displacement and communication. On the other hand, academic performance is defined as different and complex factors that act on the person who learns and has been defined as a value attributed to student achievement in academic tasks. Objective: to identify related factors between academic performance and student transportation to the "UNIANDES" University in the first quarter of the 2022-2023 period. Method: Observational, descriptive cross-sectional study carried out at the Universidad Regional Autónoma de los Andes "UNIANDES", from January to March of the year 2022. The sample consisted of 27 students who were selected from a sampling technique. intentional non-probabilistic. Variables such as age, place of residence, time in which they most frequently use private transport or public transport, estimated time of arrival at the place of education were used. Results: Regarding academic performance, 74.1% of students consider themselves with regular grades, despite feeling dangerous when transporting, they reflect that it is not the cause of their high or low academic performance, because 65% of students think it doesn't matter. Conclusions: the academic performance of the students is not related to the transportation to the university, therefore, no factors related to this problem are identified.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".