Bibliographic record
Abstract
Recently I reviewed an imaginative proposal for an $A80m funding programme to prevent chronic disease in the community. It had all the right ideas and components: good evidence for the interventions suggested and encouragement of local decision makers and partners to foster adaptation to context and sustainability. What was the evaluation budget? Ten per cent: it’s always 10%, isn’t it? That is the magic figure that seems to have been passed down through the ages to determine whether or not policies and programmes reach people and whether they work. The week before, I had reviewed a multimillion dollar protocol for a cluster randomised controlled trial of a health promotion intervention in schools. The evaluation to intervention budget ratio was five to one—almost the reverse. There are no prizes for guessing that the first proposal came from a government agency and the second from a health research agency. Yet the results produced by both are …
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 | 0.085 | 0.447 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.026 | 0.019 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.023 | 0.030 |
| Insufficient payload (model declined to judge) | 0.140 | 0.131 |
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".