The battle for Latin in UK universities: a statistical analysis of factors driving student success and failure in beginners’ Latin modules
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the UK, Latin is often seen as an elitist subject taught largely at fee-paying schools. Over the past generation, however, great strides have been made in opening up the subject to students from all backgrounds. A major hindrance to widening access to Latin at university level is that the language can often prove challenging for students. Data collected for this article reveal that only 77% of Latin students on beginners’ modules in UK universities achieved a pass. Or in other words, nearly a quarter of students embarking on the study of Latin either fail or withdraw from their module.This article seeks to investigate the problems of retention and progression in support of the battle to make the study of Latin sustainable and accessible in higher education. By analysing survey responses from 29 UK universities offering beginners’ Latin modules, it explores the impact of factors such as module weighting and duration, contact hours, class sizes, textbooks and assessment methods. In so doing, it breaks new ground in its rigorous statistical analysis of a significant set of quantitative data in an effort to improve our understanding of successful ancient language teaching, tackle real-world issues of retention, and promote student success.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it