The vocabulary barrier in the GCSE English Literature
Bibliographic record
Abstract
Every year in the United Kingdom hundreds of thousands of pupils in their last year of secondary education take a General Certificate of Secondary Education (GCSE) exam in English Literature. Yet, every year, attainment is strikingly low: one quarter of those sitting the exam fail to achieve the grade 4 required for a standard pass. This paper sought to understand the reasons for this low attainment by comparing the vocabulary used in texts on the GCSE English Literature specifications with the vocabulary encountered in books that British teenagers read for pleasure. Our analysis shows that the GCSE texts have varied but dense vocabulary, and feature many words that are not encountered in popular books or in a typical spoken language environment. The majority of these unfamiliar words are new roots whose meanings cannot be derived from their parts, suggesting that readers will need to rely on context or turn to a dictionary to interpret these. Together, our findings indicate that the GCSE texts will challenge even those pupils who read avidly in their free time, while their less able peers will be unable to access the texts. Our work suggests that a specification review is in order, and that it is critical that this review takes into account the wide variation in reading and language skills that pupils bring into the classroom.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".