Bibliographic record
Abstract
An outsider entering the village of Asten in the south of the Netherlands has a good chance of not being treated with suspicion. The majority of the local population believe that “in general, most people can be trusted.” By contrast, a visitor traveling to the districts of Wielwijk or Crabbehof in the town of Dordrecht (to the south of Rotterdam) will have to overcome more in the way of social barriers. Two-thirds of the inhabitants of these districts have formed the opinion that “you can’t be too careful in dealing with other people.” And there are other differences between Asten and these Dordrecht districts. In Asten, for example, people seek contact with other local residents easily and generally regard them as helpful. In Dordrecht this is true for only a minority of the inhabitants. Two-thirds of the inhabitants of Asten are members of an association, compared with just one-third of Dordrecht inhabitants. Attitudes to politics differ as well: In Asten the government is regarded as a reliable source of information on important social and political issues by half of the population, compared with a quarter of the population of Dordrecht. According to the survey from which we take these data, 16 percent of the citizens of Asten and 38 percent of those of Dordrecht think that whatever decisions the government makes, they are of no use for everyday life.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".