Bibliographic record
Abstract
This chapter considers the nature of evidence in education and discusses the effectiveness with which it is produced, shared and used within and across different elements of the system. It focuses on mainstream school education – that is, the system that supports children and young people though their years of compulsory school learning. It is based primarily on the English context, but also includes illustrations and developments from other UK countries. It also provides some comparative illustrations from the province of Ontario in Canada, which has a relatively well-developed system supporting educational evidence use in policy and practice. Education is compulsory in England from the ages of five to 18 (a young person must remain in some form of learning, either academic or work based, between the ages of 16 and 18). Education is a critical gateway to young people securing economic independence in adulthood, progressing in their careers and ambitions and contributing to society as effective citizens. Education has an essential role to play in creating the conditions for social mobility and equity by enabling young people to reach their full academic and personal potential and by reducing economic and social inequality. Yet there remains a stubborn and persistent ‘attainment gap’ between young people from affluent and disadvantaged backgrounds, which typically widens as they progress through their school careers (see, for example, Andrews et al, 2017). In terms of evidence, there is much that is not yet fully understood about how best to support every child to succeed, although, as we outline later, the evidence base is improving.
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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 | 0.302 | 0.641 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.043 | 0.025 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.042 | 0.042 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.015 | 0.014 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".