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Record W6887696369 · doi:10.17605/osf.io/hqvd3

The vocabulary barrier in the GCSE English Literature

2024· article· en· W6887696369 on OpenAlexaboutno aff

Bibliographic record

VenueOSF Preprints (OSF Preprints) · 2024
Typearticle
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsVocabularyReading (process)Context (archaeology)CertificateVariation (astronomy)Quarter (Canadian coin)Feature (linguistics)Subject (documents)Period (music)

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.603
Threshold uncertainty score0.658

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.7560.522

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.

Opus teacher head0.009
GPT teacher head0.282
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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