Kathleen Hall Jamieson

Authorship remains the primary currency of academic credit and a cornerstone of research integrity, yet current practices often fail to reflect the collaborative and interdisciplinary nature of modern science and questionable authorship practices persist. We argue that addressing these shortcomings is a collective responsibility shared by researchers, journals, research funders, scholarly societies, and research institutions. We examined authorship guidelines issued by journals and research institutions and found that their recommendations to researchers are highly variable. We propose that fostering a responsible authorship culture requires a shared, principle-based framework grounded in transparency, credit, and accountability. These three interconnected principles highlight when authorship practices are questionable and offer a framework for constructive reflection on the meaning of authorship. We outline practical ways research leaders can embed these principles into everyday practice by initiating early, inclusive, and fair authorship discussions and ensuring transparent description of contributions. Research institutions have a unique opportunity to inculcate good practices and lead this culture change with harmonized guidance, education, fair conflict resolution, and reform of researcher assessment. Anchoring authorship in transparency, credit, and accountability will strengthen the credibility of individual research, the fairness of recognition systems, and, ultimately, the trustworthiness of science itself.

Vigorous debate has erupted over the trustworthiness of scientific research findings in a number of domains. The question “what makes research findings trustworthy?” elicits different answers depending on whether the emphasis is on research integrity and ethics, research methods, transparency, inclusion, assessment and peer review, or scholarly communication. Each provides partial insight. We offer a systems approach that focuses on whether the research is accountable, evaluable, well-formulated, has been evaluated, controls for bias, reduces error, and whether the claims are warranted by the evidence. We tie each of these components to measurable indicators of trustworthiness for evaluating the research itself, the researchers conducting the research, and the organizations supporting the research. Our goals are to offer a framework that can be applied across methods, approaches, and disciplines and to foster innovation in development of trustworthiness indicators. Developing valid indicators will improve the conduct and assessment of research and, ultimately, public understanding and trust.

Although messenger RNA (mRNA) technology revolutionized vaccine creation, its use is threatened by unwarranted fear that DNA left over from the vaccine manufacturing process could integrate into recipients’ DNA, increasing cancer and heritable risks. Drawing on the mental model theory of reasoning, our two preregistered interventions undercut these problematic conclusions. They do so by testing the effectiveness of two mental model-based interventions juxtaposing problematic claims with visualized or verbally explained models of basic biological and vaccination systems. Study 1: a) graphically modeled how mRNA COVID-19 vaccination works (Model 1); b) verbally modeled ways in which cells protect themselves from foreign DNA (Model 2); and c) provided ancillary material designed to bolster perceptions of mRNA vaccination safety. Study 2 deployed an animation of the cell-protection model (Model 2), alone, and in combination with Study 1’s messaging. Neither the mRNA vaccine nor the DNA protection model explicitly acknowledged the problematic DNA-integration claim. Both preemptive (before) and rebuttal (after) positioning of the models were effective. Within-person analyses suggested that preemptive positioning may be somewhat more effective than rebuttal positioning. Some positive effects of exposure to the modeled knowledge messaging condition in Study 1 persisted 2 mo after exposure.

Using a weighted 2023–2025 national probability panel of US adults, we compared the perceived Credibility, Prudence, Unbiasedness, Self-Correction, and Benefit (i.e. Factors Assessing Science's Self-Presentation [FASS]) of AI scientists with those of scientists in general and climate scientists in particular. Our analysis reveals that respondents’ composite perceptions of AI scientists are the most negative of the three, a difference driven by a facet of the Prudence factor, specifically the perception that AI science is causing unintended consequences; political ideology and patterns of media exposure are substantially more predictive of perceptions of climate science and science in general than of AI; and FASS and respondent ideology predict more variance in support for federal funding of the other two than of AI.

Has the “big lie”—the false claim that the 2020 election was stolen from Donald Trump—shaped citizens’ views of the legitimacy of other US elections? We argue that it has. Those who believe Trump’s claim, whom we call election skeptics, lack confidence in elections for two interrelated reasons. First, because they think 2020 was inaccurately and unfairly conducted, they think that other elections will suffer a similar fate, and hence think these elections are illegitimate even before any votes are cast. Second, while most voters think elections are less legitimate when their preferred candidate loses, this effect will be especially large for election skeptics, because voter fraud gives them a mechanism to explain their candidate’s loss. Using an original panel dataset spanning the 2020 and 2022 elections, we show strong support for these hypotheses. This has important implications for our elections, and their legitimacy, moving forward.

Have perceptions of the U.S. Supreme Court polarized, much like the rest of American politics? Because of the Court’s unique role, for many years, it remained one of the few institutions respected by both Democrats and Republicans alike. But the Court’s dramatic shift to the right in recent years—highlighted by its Dobbs decision in 2022—potentially upends that logic. Using both eight waves of panel data and 18 nationally representative surveys spanning two decades, we show that while there was little evidence of partisan polarization in earlier years, in 2022 and 2023, such patterns are clear in favorability, trust, legitimacy, and support for reform. Factors that used to protect the Court—like knowledge about it and support for key democratic values—no longer do so. The Court has also become more important to voters, and will likely remain a political flashpoint, with disquieting implications for the Court’s place in our polity.

In recent years, many questions have been raised about whether public confidence in science is changing. To clarify recent trends in the public’s confidence and factors that are associated with these feelings, an effort initiated by the National Academies’ Strategic Council for Research Excellence, Integrity, and Trust (the Strategic Council) analyzed findings from multiple survey research organizations. The Strategic Council’s effort, which began in 2022, found that U.S. public confidence in science, the scientific community, and leaders of scientific communities is high relative to other civic, cultural, and governmental institutions for which researchers regularly collect such data. However, confidence in these institutions has fallen during the previous 5 years. Science’s decline, while real, is similar to or less than that in the other groups. A recent study goes into greater detail by exploring public views of science. From these data, we observe that many of the surveyed U.S. public question the extent to which scientists share their values or overcome personal biases when presenting conclusions. At the same time, large majorities agree on certain types of actions that they want scientists to take. For example, 84% respond that it is “somewhat important” or “very important” for scientists to disclose their funders. Ninety-two percent (92%) offer the same responses to scientists “being open to changing their minds based on new evidence.” Collectively, these data clarify how the U.S. public views science and scientists. They also suggest actions that can affect public confidence in science and scientists in the years to come.

A confirmatory factor analysis (CFA) of responses to 13 questions from a 2022 national probability sample of 1,154 US adults supported the existence of five factors that we argue assess perceptions of Factors Assessing Science’s Self-Presentation (FASS). These factors also predict support for increasing federal funding of science and, separately, supporting federal funding of basic research. Each of the factors reflects perceptions of a key facet of scientists’ self-presentation, science/scientists’ adherence to professed norms, or science’s benefits: specifically, that scientists are Credible, Prudent, and Unbiased and that science is Self-Correcting and Beneficial. The FASS model explained 40.6% of the variance in support for increasing federal funding for science and 33.7% in support for basic research. For both dependent variables, conservatives were less likely to be supportive when they perceived that science/scientists fail to overcome biases. The interactions between political ideology and both Prudence and Beneficial, however, were significant only when predicting Basic Research support. In that case, there were no differences between conservatives and liberals when perceptions of benefit were low, but when high, liberals’ perception of benefit had a stronger association with support for funding than conservatives’. Among those perceiving that scientists lack prudence, liberals were more likely to support funding basic research than conservatives, but the difference disappeared when perceptions of prudence were very high. The factors could serve as across-time indicators of the public’s assessment of the state of science.

Although polling is not irredeemably broken, changes in technology and society create challenges that, if not addressed well, can threaten the quality of election polls and other important surveys on topics such as the economy. This essay describes some of these challenges and recommends remediations to protect the integrity of all kinds of survey research, including election polls. These 12 recommendations specify ways that survey researchers, and those who use polls and other public-oriented surveys, can increase the accuracy and trustworthiness of their data and analyses. Many of these recommendations align practice with the scientific norms of transparency, clarity, and self-correction. The transparency recommendations focus on improving disclosure of factors that affect the nature and quality of survey data. The clarity recommendations call for more precise use of terms such as “representative sample” and clear description of survey attributes that can affect accuracy. The recommendation about correcting the record urges the creation of a publicly available, professionally curated archive of identified technical problems and their remedies. The paper also calls for development of better benchmarks and for additional research on the effects of panel conditioning. Finally, the authors suggest ways to help people who want to use or learn from survey research understand the strengths and limitations of surveys and distinguish legitimate and problematic uses of these methods.