Bibliographic record
Abstract
Nowadays, due to various reasons, more students are beginning to pursue higher academic degrees, such as master's degrees. This article discusses the factors that influence the chances of graduate admissions. The aim is to quantify each determinant by establishing a linear model, thus helping everyone understand how much each factor can affect the admission chance. The dataset used in this article comes from the Kaggle, which includes eight variables and 400 observations. This article establishes several multiple linear models using the smallest AIC selection, the smallest BIC selection, and the LASSO selection. Models are screened based on some indicative values such as R2adj, SSres, and R2. After establishing the final model, assumptions (Normality, homoscedasticity, multicollinearity, linear relationship) and prediction errors are checked to verify the model's effectiveness. The article ultimately finds that every predictor positively correlates with the admission chances. It means that the more achievements an applicant has, the higher the chance of admission. This conclusion is consistent with our initial hypothesis. The final results can help applicants understand the importance of each application material (predictor). By inputting their existing achievements for each predictor into the model, they can predict their chances of admission, identify deficiencies, and work on improvements.
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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".