The Study on Gender Differences as Factor and the Correlation between Grade Point Average in 6th Grade Science and 7th Grade Science-Chemistry on First Quarter among Grade 7- Anthurium at Passi National High School, Passi City, Iloilo
Bibliographic record
Abstract
This descriptive-correlational study was designed to determine the factors that influence grades in the 7th grade Science first quarter and to assess the relationship between the students’ average grades in 6th grade in Science and 7th grade first quarter Science (Chemistry) among 50 Grade 7 –Anthurium at Passi National high School, Passi City, Iloilo for the school year 2016-2017. The respondents were grouped according to gender and average grades in 6th grade Science. The students’ Form 137-A were taken at the school registrar’s office for data on student grades. The statistical treatments used were means, standard deviations and percentages for descriptive analysis. The t-Test, one-way ANOVA and the Pearson r set at .05 alpha, were employed as inferential statistics. The results showed that no significant differences on the subjects’ grade in 7th grade in Science first quarter (Chemistry) when classified according to male and female; low, average, and high – achieving students’; and no significant correlation existed between the students’ average grades in their 6th grade Science and 7th Science (Chemistry) grades.
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 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 | 0.000 | 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) | 0.004 | 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".