Briefing No. 7 - Attainment and Assessment
Bibliographic record
Abstract
Pupils from independent schools were much more likely to report that their Teacher Assessed Grades (TAGs) were higher than they expected (43%) than those in state schools (34% for those in state grammars and 29% in state comprehensives). They were also much less likely to report that they were lower than they expected (at 7%, compared to 15% of those in state grammars and 23% in state comprehensives). \n \nA third of young people reported that they felt that teachers were biased against certain groups in their teacher assessment. This figure was higher among those from ethnic minority backgrounds and lower among those with more socio-economically advantaged backgrounds. \n \nPupils who had particularly disrupted experiences during the COVID-19 pandemic received lower GCSE Teacher Assessed Grades (TAGs) than their peers whose disruption was more moderate. \n \nOne-to-one or small group tutoring as catch-up provision was most likely to be offered to those from less advantaged backgrounds and those who had lower prior attainment. Boys were more likely to be offered tutoring but, as they were less likely to take it up, there was no gender difference in reported receipt of tutoring. \n \nThose who received one-to-one and small group tutoring appeared to perform slightly better in their GCSE TAGs than their peers who were offered this tutoring but did not take it up. However, only just over a quarter of the sample reported that they have received one-to-one or small group tutoring, meaning it is unlikely to have made a big difference to learning lost at the cohort level.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.418 | 0.198 |
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".