Bibliographic record
Abstract
Most postsecondary educational institutions employ some type of language proficiency assessment for international applicants to assess their language skills (Alderson, Krahnke, & Stansfield, 1987; Chalhoub Deville & Turner, 2000; Kahn, Butler, Weigle, & Sato, 1994; Paltridge, 1992; Person, 2002; Rees, 1999; Roemer, 2002; Seaman & Hayward, 2000). The performance of these applicants is of interest to adminis trators, faculty, staff, and researchers alike, with gender variations being one issue often studied. These types of studies tend to compare the performance of females with males in terms of mean test scores by subtest and/or total test score, and in some cases by specific test questions or types of questions. The score differences are often reported as raw score differences, but to enhance comparability between tests, the differences also can be expressed in a standardized form such as a standard mean difference or a percent difference. The standard mean difference, denoted by D, is considered by Willingham and Cole (1997) in their meta-analysis of gender and assessment as one of the most common measurements. It is calculated by subtracting the male mean score from the female mean score and dividing the difference by the average standard deviation (SD):
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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".