Bibliographic record
Abstract
How much can we reasonably expect from research in education? There are many reasons to think the effects could be both powerful and positive. The call to make more use of research evidence does not in any way conflict with professional autonomy; if anything, it reinforces it.There are many areas of education where we do not have enough evidence to be confident about what to do. However, there are areas where we do have enough knowledge and yet are not applying it broadly. The take-up of evidence greatly depends on professionals’ belief that their work should be guided by reliable knowledge, yet that belief is itself largely created by social practices and communication patterns. Many of the necessary elements to do so are simply not there today in most schools or school systems. Many of these features could be reinforced with relatively little effort with good results for students.
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 | 0.205 | 0.592 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.018 | 0.010 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.021 | 0.024 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.022 | 0.020 |
| Insufficient payload (model declined to judge) | 0.017 | 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; 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".