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Record W4412301546

Capturing Change in Science, Technology, and Innovation:Improving Indicators to Inform Policy

2014· report· en· W4412301546 on OpenAlexaboutno aff
Robert E. Litan, Andrew Wyckoff, Carter Walter Bloch, Nicholas Chrisman, Carl J. Dahlman, Geoff M. Davis, Katherine G. Frase, Barbara M. Fraumeni, Richard B. Freeman, Fred Gault, David B. Goldston, Michael Mandel, John E. Rolph, Leland Wilkinson, Kaye Husbands Fealing

Bibliographic record

Venuenot available
Typereport
Languageen
FieldBusiness, Management and Accounting
TopicUniversity-Industry-Government Innovation Models
Canadian institutionsnot available
FundersDivision of Mathematical SciencesDirectorate for Computer and Information Science and EngineeringAustralian GovernmentNational Institutes of HealthNational Science Foundation
KeywordsScience policyData scienceKnowledge managementBusinessComputer sciencePolitical sciencePublic administration
DOInot available

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.160
metaresearch head score (Gemma)0.226
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.985
Threshold uncertainty score0.847

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1600.226
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0150.021
Science and technology studies0.0030.005
Scholarly communication0.0200.023
Open science0.0050.008
Research integrity0.0050.010
Insufficient payload (model declined to judge)0.0070.003

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.

Opus teacher head0.041
GPT teacher head0.277
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
DomainEvaluation
GenreMethods

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".

Quick stats

Citations3
Published2014
Admission routes1
Has abstractyes

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