Comparative coh-metrix analysis of reading comprehension texts: Unified (Russian) state exam in English vs cambridge first certificate in English
Bibliographic record
Abstract
© Canadian Center of Science and Education. The article summarizes the results of the comparative study of Reading comprehension texts used in B2 level tests: Unified (Russia) State Exam in English (EGE) and Cambridge First Certificate in English (FCE). The research conducted was mainly focused on six parameters measured with the Coh-Metrix, a computational tool producing indices of the linguistic and discourse representations of a text: narrativity, syntactic simplicity, word concreteness, referential cohesion, deep cohesion, Flesh Reading Ease. The research shows that the complexity of EGE texts caused by lower than in FCE texts cohesion is balanced with a simpler than in FCE texts syntax and higher narrativity thus resulting in about the same text complexity of the two sets of texts studied. EGE and FCE texts demonstrate correspondence to grade six and very similar Means of Flesh Reading Ease (FCE Mean is 71.06; EGE Mean is 78.25) which fit the band FAIRLY EASY.
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".