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Record W4210667022 · doi:10.4236/jss.2022.101028

Effects of School Proximity on Students’ Performance in Mathematics

2022· article· en· W4210667022 on OpenAlexaboutno aff
Emerson D. Peteros, Shiela C. Ypil, John V. De Vera, Gerly A. Alcantara, Margie Fulgencio, Dennis Plando, Larry B. Peconcillo

Bibliographic record

VenueOpen Journal of Social Sciences · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationSimple random sampleGovernment (linguistics)Quarter (Canadian coin)Descriptive statisticsPsychologyMathematicsGeographySociologyStatisticsPopulation

Abstract

fetched live from OpenAlex

This research investigated the school proximity and the Grade 7 students’ academic performance in Math of a mountain barrio public national high school in Pinamungajan, Cebu, Philippines. There were 171 respondents who were identified using simple random sampling. They answered a survey questionnaire describing their proximity to the school while their Fourth Quarter Grades were used to assess their academic performance in Math. Data gathered were treated using descriptive and inferential statistics. Results revealed that most of the students are very far from school which they have to walk to reach school. Most of their houses are not accessible to the road. They had a very satisfactory performance in Math. Moreover, there was a significant relationship between the students’ distance to school and their academic performance in Math. However, no significant relationships between the student’s mode of transportation, house accessibility to the road, and their academic performance in Math were found. Thus, it is recommended that school administrators and teachers design programs that would address students’ challenges in attending school relative to their house to school distance while the government provides infrastructures that would address concerns on long-distance travel of the students.

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)0.005
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 score0.021
Threshold uncertainty score0.875

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.047
GPT teacher head0.379
Teacher spread0.332 · 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

Citations16
Published2022
Admission routes1
Has abstractyes

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