An analysis of gender and performance for students writing the British Columbia grade 12 provincial exams
Bibliographic record
Abstract
Research on gender performance generally focuses on how boys and girls perform on standardized tests. Recent trends indicate that boys are doing worse than girls statistically in the areas of literacy and to a certain degree in the sciences. In British Columbia, students write provincial exams (standardized tests) at the end of a course. The exams count as 40% of their final grade. This study focuses on assessing whether there are significant differences in the mean scores of boys and girls over ten school years, from 1995-2005, in six provincial exam subjects: Biology 12, Communications 12, English 12, History 12, Principles of Mathematics 12, and Physics 12. Data were obtained from the Ministry of Education through Edudata. Independent t-tests were chosen to assess whether there were significant differences between male scores and female scores for each of the exams written for each school year. Findings from these analyses indicated that there were significant differences on mean scores in exams between males and females. Female students perform higher in Physics 12, Principles of Mathematics 12, English 12, and Communications 12 while male students perform higher in History 12. In Biology 12, there were years where males performed higher and other years where females performed higher.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".