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Record W7061771163

Related factors between academic performance and transportation of students to the University "UNIANDES"

2023· article· en· W7061771163 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2023
Typearticle
Languageen
FieldEngineering
TopicThermal Analysis in Power Transmission
Canadian institutionsnot available
Fundersnot available
KeywordsFeelingSample (material)Academic achievementSet (abstract data type)Academic yearQuarter (Canadian coin)Learning development
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.636

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.140
GPT teacher head0.475
Teacher spread0.335 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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