Steering Research Toward Policy: The Case of Social Science and Environmental Change
Bibliographic record
Abstract
In examining the making and management of targeted programs of social environmental research in the United States, Germany, the Netherlands, Britain, Norway, and Canada, this chapter shows their role in generating policy relevant research activity in new interdisciplinary fields, and in forging closer links between researchers and research users. Nevertheless, the programs have one thing in common: all represent a concerted effort to foster social science relevant to the broadly-defined policy problem of understanding and managing global environmental change. At senior level, German social scientists contribute to directly-funded government research initiatives at national and local levels and, as in the United States, there are high-level linkages between policymakers and the research council. The new linkages model appears to offer more opportunities for steering agendas and encouraging policy relevance, yet it is important to acknowledge that program managers are still ultimately dependent on the research community.
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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 | 0.046 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.008 | 0.039 |
| Scholarly communication | 0.021 | 0.016 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".