Modeling the Impact of Science Policies on Scientific Workforce Growth

模拟科学政策对科学劳动力增长的影响

• 批准号: 8184363
• 负责人: Antonio P Sanfilippo
• 金额: $ 30.96万
• 依托单位: BATTELLE PACIFIC NORTHWEST LABORATORIES
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-19 至 2015-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8184363
• 关键词: Academia; Achievement; Address; Affect; Area; Behavioral; Calibration; Collection; Consultations; Data; Dependency; Development; Education; Effectiveness; Engineering; Environment; Evaluation; Goals; Government; Growth; Income; Industry; Institution; Intervention; Investments; Judgment; Knowledge; Laboratories; Leadership; Maintenance; Marketing; Measures; Methods; Modeling; Occupations; Outcome; Output; Play; Policies; Population; Research; Research Personnel; Role; Science; Science Policy; Simulate; Solutions; Structure; System; Techniques; Technology; Testing; Underrepresented Minority; United States National Institutes of Health; Visual; Work; cohort; design; evidence base; federal policy; innovation; new growth; programs; social; socioeconomics; tool

DESCRIPTION (provided by applicant): The development of a strong, talented, diverse, innovative and stable scientific workforce is critical for the U.S. to maintain global leadership and competitiveness in science and technology. The achievement of such a goal requires a clear understanding of how science and innovation policies by federal, state and local constituencies including government, academia and industry impact on the creation and growth of the U.S. scientific workforce. Our goal is to develop an evidence-based model of how science and innovation policies affect the scientific workforce to help policymakers and other stakeholders design and test interventions that are more effective in promoting sustainable growth of jobs in science and engineering. More specifically, the model will evaluate how research and educational investments by sponsoring institutions have favored or inhibited the growth of the scientific workforce within the last 10 years. We will concentrate on the biomedical field. We will achieve the proposed through the following four specific aims. Specific aim 1: Develop a Knowledge Encapsulation Framework to enable the collection, analysis, vetting and dissemination of evidence and subject matter expertise relevant to (1) the biomedical workforce, and (2) policies and investments in research and education within the biomedical area. Specific aim 2: Use evidence and subject matter expertise relevant to the scientific workforce, science and innovation policies, and investments in the biomedical field to develop, calibrate and evaluate a dynamic evidence-based model of population change in the scientific workforce. Specific aim 3: Develop an analytical gaming environment in which subject matter experts, analysts and policymakers interact through role-playing to evaluate plausible scenarios emerging from simulated dependencies generated by the model developed in specific aim 2. Specific aim 4: Evaluate the effectiveness of the model and game developed in specific aims 2-3 as a proactive reasoning tool against historical data and subject matter experts' judgments. PUBLIC HEALTH RELEVANCE: A strong and stable scientific workforce is necessary for our social and economy prosperity, since growth in U.S. income per capita depends heavily on technological change. The proposed research will help understand how ongoing and prospected science and innovation policies may impact on the creation and maintenance of jobs in science and engineering and the ensuing socioeconomic growth.

描述（由申请人提供）：发展一支强大、有才能、多样化、创新和稳定的科学队伍对美国保持全球科学技术领导地位和竞争力至关重要。要实现这一目标，需要清楚地了解联邦、州和地方选区（包括政府、学术界和工业界）的科学和创新政策如何影响美国科学劳动力的创造和增长。我们的目标是开发一个基于证据的科学和创新政策如何影响科学劳动力的模型，以帮助决策者和其他利益相关者设计和测试更有效地促进科学和工程领域就业可持续增长的干预措施。更具体地说，该模型将评估赞助机构的研究和教育投资在过去10年里是如何促进或抑制科学劳动力的增长的。我们将把重点放在生物医学领域。我们将通过以下四个具体目标来实现上述目标。具体目标1：制定一个知识封装框架，以便能够收集、分析、审查和传播与(1)生物医学工作人员和(2)生物医学领域研究和教育的政策和投资相关的证据和主题专业知识。具体目标2：利用与科学劳动力、科学和创新政策以及生物医学领域投资相关的证据和主题专业知识，开发、校准和评估科学劳动力人口变化的动态循证模型。具体目标3：开发一个分析游戏环境，在这个环境中，主题专家、分析师和政策制定者通过角色扮演进行互动，以评估由具体目标2中开发的模型产生的模拟依赖所产生的合理情景。具体目标4：根据历史数据和主题专家的判断，评估具体目标2-3中开发的模型和游戏作为主动推理工具的有效性。