Capturing Change in Science, Technology, and Innovation:Improving Indicators to Inform Policy
Bibliographic record
Abstract
physics, political science, psychology, statistics, and visual analytics.The panel also reflects the international nature of the topic, with members from Canada, Denmark, France, and the Netherlands.In undertaking this study, the panel first relied on users, experts, and written reports and peer-reviewed articles to establish current and anticipated user needs for STI indicators.Second, the panel recognized that no one model informs the types of indicators NCSES needs to produce.Policy questions served as an important guide to the panel's review, but the study was also informed by systems approaches and international comparability.Third, it was important to identify data resources and tools NCSES could exploit to develop its indicators program.Understanding the network of inputs-including data from NCSES surveys, other federal agencies, international organizations, and the private sector-that can be tapped in the production of indicators gave rise to a set of recommendations for working with other federal agencies and public and private organizations.Fourth, the panel did not limit its recommendations to indicators but also addressed processes for prioritizing data development and the production of indicators in the future, because it was clear that the changing environment in which NCSES operates is a key determinant of the agency's priorities from year to year.Internal processes that are observant, networked, and statistically and analytically balanced are important for NCSES's indicators program.On request of the sponsor, an interim report was published in February 2012, summarizing the panel's early findings and recommendations.The recommendations offered in this report expand on those of the interim report.They are intended to serve as the basis for a strategic program of work that will enhance NCSES's ability to produce indicators that capture change in STI to inform policy and optimally meet the needs of its user community.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.045 | 0.042 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".