Bibliographic record
Abstract
This paper is on the seasonality of GPA. It is exceptionally under-researched, which is odd because of its potential applicability. The primary purpose of this paper is to build a framework on which future research may be conducted concerning the determinants of GPA through an intra-year lens. This may be of interest to universities, as knowing how GPA varies between seasons may be useful in their use of resources or course scheduling. \n I acquired most of my data from the Center for the Study of Student Life (CSSL); the rest (precipitation) was from the National Oceanic and Atmospheric Administration (NOAA). The CSSL provided me with data on GPA, class rank, credit hours, ethnicity, birthplace, year, and quarter. The NOAA provided data on precipitation readings gathered at the OSU airport.\n My findings were that fall and winter quarters are both negatively correlated with GPA when compared to spring quarter when using fixed-effects. Additionally, the coefficients associated with fall and winter quarters could not be proven to be statistically different from each other, so, while they are both worse than spring quarter performance, we cannot say that there’s a difference between the two.\n This paper asks many questions, the most important/intriguing of which are: Why does the most recent academic year have the lowest average GPA? Why does there exist a spring quarter spike? These are very important questions, and I hope to answer them with further research.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".