Reducing socioeconomic health differencesbetween 2000 and 2020: Monitoring setup
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Dutch government aims to reduce the socioeconomic health differences (SEGV) with a quarter of the current difference by the year 2020. To this purpose the health of different socioeconomic status groups needs to be monitored. The SEGV monitor will periodically report on the extent of socioeconomic differences at the national level in the Netherlands. Health determinants, such as health-related behaviour, environmental factors and healthcare use will also be monitored. Because many policies and interventions are developed and carried out at the local level, it would be desirable to be able to draw conclusions on socioeconomic health differences at this level or to have information on the development in health in certain neighbourhoods. The SEGV monitor will make use of existing data sources with nation-wide coverage to generate a representative and valid picture of the development of socioeconomic health differences in the Netherlands. The SEGV monitor is to report on socioeconomic differences in health and its determinants every four years. Each four-yearly report will be accompanied by an elaborate supplement on one specific current subject. Results of the SEGV monitor will be accessible through the Internet via a thematic link on the Dutch-language website, 'Nationaal Kompas Volksgezondheid'.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it