The Influence of the Availability of Facilities and Infrastructure on Student Motivation at SMA Negeri 1 Parmaksian
Bibliographic record
Abstract
Abstract: This research aims to analyze the influence of the availability of facilities and infrastructure on student learning motivation at SMA N 1 Parmaksian. The method used is a quantitative inferential statistical method. The population of this study consisted of 195 students at SMA Negeri 1 Parmaksian, with a sample of 66 people. Data was collected through a closed questionnaire with 42 statement items. The results of the research show that there is a positive and significant influence on the availability of facilities and infrastructure on student learning motivation at SMA Negeri 1 Parmaksian. This research uses two types of analysis requirements, namely the normality test and the linearity test. The test results show that there is a positive relationship between the variables X and Y with a value of rcount = 0.404 > ttable (α = 0.05, n = 66) 0.235. Apart from that, the significance test shows that there is a significant relationship between the variables The hypothesis was tested through a regression equation test which produced the equation ỳ = 41.957 + 0.475 and the regression coefficient of determination r2 = 0.163. Hypothesis testing with the F test shows Fcount > Ftable = 12.478 > 4.00.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".