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This framework builds on the assessment of federal scientific integrity policies and practices described in the January 2022 report, Protecting the Integrity of Government Science, and draws from extensive input from federal agencies, as well as from across sectors, including academia, the scientific community, public interest groups, and industry. It has several key components that federal departments and agencies will use to improve scientific integrity policies and practices, including:

The framework requires all agencies to designate a scientific integrity official, and agencies that fund, conduct, or oversee research to designate a chief science officer, and it establishes the National Science and Technology Council (NSTC) Subcommittee on Scientific Integrity to oversee implementation of the framework, and evaluate agency progress.

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The framework was developed following a robust effort to study and improve scientific integrity policies and outcomes, and extensive engagement with stakeholders inside and outside of the federal government starting in May 2021. This process included engaging 30 federal agencies, and processing feedback from over 1,000 individuals and organizations through three listening sessions, three roundtables, and two requests for information.

Strong policies and effective practices protecting scientific integrity are essential for the development of evidence-based policies. By bolstering these policies and practices across the federal government, this first-of-its-kind framework will strengthen the ability of agencies and federal scientists to produce critical scientific information for evidence-based policymaking that can help make our nation healthier, safer, more prosperous, and more secure.

In computer systems, a framework is often a layered structure indicating what kind of programs can or should be built and how they would interrelate. Some computer system frameworks also include actual programs, specify programming interfaces, or offer programming tools for using the frameworks. A framework may be for a set of functions within a system and how they interrelate; the layers of an operating system; the layers of an application subsystem; how communication should be standardized at some level of a network; and so forth. A framework is generally more comprehensive than a protocol and more prescriptive than a structure.

The MIT Framework creates a mechanism for ensuring scholarly research outputs are openly and equitably available to the broadest and most inclusive audience possible, while also providing valued services to our community. The vision we seek to advance through the application of this framework is one in which enduring, abundant, equitable, and meaningful access to scholarship serves to empower and inspire humanity.

The framework is a vehicle to encourage NIMHD- and NIH-supported research that addresses the complex and multi-faceted nature of minority health and health disparities, including research that spans different domains of influence (biological, behavioral, physical/built environment, sociocultural environment, health care system) as well as different levels of influence (individual, interpersonal, community, societal) within those domains.

The framework also provides a classification structure that facilitates analysis of the NIMHD and NIH minority health and health disparities research portfolios to assess progress, gaps, and opportunities. Examples of factors are provided within each cell of the framework (e.g., family microbiome within the interpersonal-biological cell). These factors are not intended to be exhaustive. Health disparity populations, as well as other features of this framework, may be adjusted over time.

The Framework offered here is called a framework intentionally because it is based on a cluster of interconnected core concepts, with flexible options for implementation, rather than on a set of standards or learning outcomes, or any prescriptive enumeration of skills. At the heart of this Framework are conceptual understandings that organize many other concepts and ideas about information, research, and scholarship into a coherent whole. These conceptual understandings are informed by the work of Wiggins and McTighe,2 which focuses on essential concepts and questions in developing curricula, and also by threshold concepts3 which are those ideas in any discipline that are passageways or portals to enlarged understanding or ways of thinking and practicing within that discipline. This Framework draws upon an ongoing Delphi Study that has identified several threshold concepts in information literacy,4 but the Framework has been molded using fresh ideas and emphases for the threshold concepts. Two added elements illustrate important learning goals related to those concepts: knowledge practices,5 which are demonstrations of ways in which learners can increase their understanding of these information literacy concepts, and dispositions,6 which describe ways in which to address the affective, attitudinal, or valuing dimension of learning. The Framework is organized into six frames, each consisting of a concept central to information literacy, a set of knowledge practices, and a set of dispositions. The six concepts that anchor the frames are presented alphabetically:

To help managers ensure accountability and responsible use of artificial intelligence (AI) in government programs and processes, GAO developed an AI accountability framework. This framework is organized around four complementary principles, which address governance, data, performance, and monitoring. For each principle, the framework describes key practices for federal agencies and other entities that are considering, selecting, and implementing AI systems. Each practice includes a set of questions for entities, auditors, and third-party assessors to consider, as well as procedures for auditors and third- party assessors.

GAO's objective was to identify key practices to help ensure accountability and responsible AI use by federal agencies and other entities involved in the design, development, deployment, and continuous monitoring of AI systems. To develop this framework, GAO convened a Comptroller General Forum with AI experts from across the federal government, industry, and nonprofit sectors. It also conducted an extensive literature review and obtained independent validation of key practices from program officials and subject matter experts. In addition, GAO interviewed AI subject matter experts representing industry, state audit associations, nonprofit entities, and other organizations, as well as officials from federal agencies and Offices of Inspector General.

This is a summary of the main components of the Preparedness Framework (Beta), and we encourage you to read the complete version. This framework is the initial Beta version that we are adopting, and is intended to be a living document. We expect it to be updated regularly as we learn more and receive additional feedback. We welcome your thoughts at pf@openai.com.

The framework is guided by the principal policy objectives of the United States as laid out in the Executive Order on Ensuring Responsible Development of Digital Assets (March 9, 2022) and tailored to reflect the international aspects of our work:

Regional and Bilateral Engagements: The United States will identify where existing regional and bilateral engagements can be strengthened and, where appropriate, ramp up engagement with new partners to achieve our objectives with respect to digital assets. The United States will use these engagements under a coordinated framework for prioritization across departments and agencies to explore potential opportunities and risks of digital assets, engage in information sharing, drive the adoption and implementation of policies including with respect to AML/CFT; and provide technical assistance, where appropriate. The United States will explore opportunities for joint experimentation on digital assets technologies, market innovations and CBDCs, with this core set of allies and partners to increase our shared learning about ways to develop systems that meet our shared policy objectives.

The proposal is part of a wider AI package, which also includes the updated Coordinated Plan on AI. Together, the Regulatory framework and Coordinated Plan will guarantee the safety and fundamental rights of people and businesses when it comes to AI. And, they will strengthen uptake, investment and innovation in AI across the EU.

The conceptual framework for biodiversity and ecosystems services is to support the analytical work of the Platform, to guide the development, implementation and evolution of its work programme, and to catalyse a positive transformation in the elements and interlinkages that are the causes of detrimental changes in biodiversity and ecosystems and subsequent loss of their benefits to present and future generations. This conceptual and analytical tool is to underpin all IPBES functions and provide structure and comparability to the syntheses that IPBES will produce at different spatial scales, on different themes, and in different regions.

Salient innovative aspects of the conceptual framework are its transparent and participatory construction process and its explicit consideration of diverse scientific disciplines, stakeholders, and knowledge systems, including indigenous and local knowledge. In this sense, the conceptual framework is a tool for the achievement of a shared working understanding across different disciplines, knowledge systems and stakeholders that are expected to be active participants in the Platform, and is intended to be a basic common ground, general and inclusive, for coordinated action towards the achievement of the ultimate goal of the Platform.

These elements and their interrelations are further explained in the conceptual framework adopted by IPBES and in two scientific publications building on that decision. Further, a set of e-learning modules explaining the conceptual framework has been developed Sub-Global Assessment Network (SGAN), a collaborative supporter of IPBES. 2351a5e196