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1. The Effects of Gist Information and Scientific Quality on Damages in a Civil Trial

   Dellapaolera, K.S. ( March 2020 , Annual Conference of the American Psychology-Law Society.)

   Abstract: 100 words Jurors are increasingly exposed to scientific information in the courtroom. To determine whether providing jurors with gist information would assist in their ability to make well-informed decisions, the present experiment utilized a Fuzzy Trace Theory-inspired intervention and tested it against traditional legal safeguards (i.e., judge instructions) by varying the scientific quality of the evidence. The results indicate that jurors who viewed high quality evidence rated the scientific evidence significantly higher than those who viewed low quality evidence, but were unable to moderate the credibility of the expert witness and apply damages appropriately resulting in poor calibration. Summary: <1000 words Jurors and juries are increasingly exposed to scientific information in the courtroom and it remains unclear when they will base their decisions on a reasonable understanding of the relevant scientific information. Without such knowledge, the ability of jurors and juries to make well-informed decisions may be at risk, increasing chances of unjust outcomes (e.g., false convictions in criminal cases). Therefore, there is a critical need to understand conditions that affect jurors’ and juries’ sensitivity to the qualities of scientific information and to identify safeguards that can assist with scientific calibration in the courtroom. The current project addresses these issues with an ecologically valid experimental paradigm, making it possible to assess causal effects of evidence quality and safeguards as well as the role of a host of individual difference variables that may affect perceptions of testimony by scientific experts as well as liability in a civil case. Our main goal was to develop a simple, theoretically grounded tool to enable triers of fact (individual jurors) with a range of scientific reasoning abilities to appropriately weigh scientific evidence in court. We did so by testing a Fuzzy Trace Theory-inspired intervention in court, and testing it against traditional legal safeguards. Appropriate use of scientific evidence reflects good calibration – which we define as being influenced more by strong scientific information than by weak scientific information. Inappropriate use reflects poor calibration – defined as relative insensitivity to the strength of scientific information. Fuzzy Trace Theory (Reyna & Brainerd, 1995) predicts that techniques for improving calibration can come from presentation of easy-to-interpret, bottom-line “gist” of the information. Our central hypothesis was that laypeople’s appropriate use of scientific information would be moderated both by external situational conditions (e.g., quality of the scientific information itself, a decision aid designed to convey clearly the “gist” of the information) and individual differences among people (e.g., scientific reasoning skills, cognitive reflection tendencies, numeracy, need for cognition, attitudes toward and trust in science). Identifying factors that promote jurors’ appropriate understanding of and reliance on scientific information will contribute to general theories of reasoning based on scientific evidence, while also providing an evidence-based framework for improving the courts’ use of scientific information. All hypotheses were preregistered on the Open Science Framework. Method Participants completed six questionnaires (counterbalanced): Need for Cognition Scale (NCS; 18 items), Cognitive Reflection Test (CRT; 7 items), Abbreviated Numeracy Scale (ABS; 6 items), Scientific Reasoning Scale (SRS; 11 items), Trust in Science (TIS; 29 items), and Attitudes towards Science (ATS; 7 items). Participants then viewed a video depicting a civil trial in which the defendant sought damages from the plaintiff for injuries caused by a fall. The defendant (bar patron) alleged that the plaintiff (bartender) pushed him, causing him to fall and hit his head on the hard floor. Participants were informed at the outset that the defendant was liable; therefore, their task was to determine if the plaintiff should be compensated. Participants were randomly assigned to 1 of 6 experimental conditions: 2 (quality of scientific evidence: high vs. low) x 3 (safeguard to improve calibration: gist information, no-gist information [control], jury instructions). An expert witness (neuroscientist) hired by the court testified regarding the scientific strength of fMRI data (high [90 to 10 signal-to-noise ratio] vs. low [50 to 50 signal-to-noise ratio]) and gist or no-gist information both verbally (i.e., fairly high/about average) and visually (i.e., a graph). After viewing the video, participants were asked if they would like to award damages. If they indicated yes, they were asked to enter a dollar amount. Participants then completed the Positive and Negative Affect Schedule-Modified Short Form (PANAS-MSF; 16 items), expert Witness Credibility Scale (WCS; 20 items), Witness Credibility and Influence on damages for each witness, manipulation check questions, Understanding Scientific Testimony (UST; 10 items), and 3 additional measures were collected, but are beyond the scope of the current investigation. Finally, participants completed demographic questions, including questions about their scientific background and experience. The study was completed via Qualtrics, with participation from students (online vs. in-lab), MTurkers, and non-student community members. After removing those who failed attention check questions, 469 participants remained (243 men, 224 women, 2 did not specify gender) from a variety of racial and ethnic backgrounds (70.2% White, non-Hispanic). Results and Discussion There were three primary outcomes: quality of the scientific evidence, expert credibility (WCS), and damages. During initial analyses, each dependent variable was submitted to a separate 3 Gist Safeguard (safeguard, no safeguard, judge instructions) x 2 Scientific Quality (high, low) Analysis of Variance (ANOVA). Consistent with hypotheses, there was a significant main effect of scientific quality on strength of evidence, F(1, 463)=5.099, p=.024; participants who viewed the high quality evidence rated the scientific evidence significantly higher (M= 7.44) than those who viewed the low quality evidence (M=7.06). There were no significant main effects or interactions for witness credibility, indicating that the expert that provided scientific testimony was seen as equally credible regardless of scientific quality or gist safeguard. Finally, for damages, consistent with hypotheses, there was a marginally significant interaction between Gist Safeguard and Scientific Quality, F(2, 273)=2.916, p=.056. However, post hoc t-tests revealed significantly higher damages were awarded for low (M=11.50) versus high (M=10.51) scientific quality evidence F(1, 273)=3.955, p=.048 in the no gist with judge instructions safeguard condition, which was contrary to hypotheses. The data suggest that the judge instructions alone are reversing the pattern, though nonsignificant, those who received the no gist without judge instructions safeguard awarded higher damages in the high (M=11.34) versus low (M=10.84) scientific quality evidence conditions F(1, 273)=1.059, p=.30. Together, these provide promising initial results indicating that participants were able to effectively differentiate between high and low scientific quality of evidence, though inappropriately utilized the scientific evidence through their inability to discern expert credibility and apply damages, resulting in poor calibration. These results will provide the basis for more sophisticated analyses including higher order interactions with individual differences (e.g., need for cognition) as well as tests of mediation using path analyses. [References omitted but available by request] Learning Objective: Participants will be able to determine whether providing jurors with gist information would assist in their ability to award damages in a civil trial. 

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2. Impermanence and failure: the legacy of conservation-based payments in Sumatra, Indonesia

   https://doi.org/10.1088/1748-9326/ac6437  

   Erbaugh, James T. ( April 2022 , Environmental Research Letters)

   Projects that pay communities or individuals to conserve natural areas rarely continue indefinitely. When payments cease, the behaviors they motivate can change. Previous research on conservation-based payments recognizes the impermanence of conservation success, but it does not consider the legacy of payments that failed to effect change. This research assesses impermanence and failure by investigating the legacy of village-level conservation payments made through one of the largest Integrated Conservation and Development Projects in Indonesia. The Kerinci-Seblat Integrated Conservation and Development Project aimed to conserve forest area and promote local development through voluntary conservation agreements (VCAs) that provided payments for pro-conservation pledges and activities from 2000 through 2003. Project documentation and previous research find that payments failed to incentivize additional forest conservation, producing nonsignificant differences in forest-cover change during the project period. To examine the legacy of these payments in the post-project period, this research uses matched difference-in-differences and triple differences models to analyze forest cover change in villages (n= 263) from 2000 through 2016 as well as matched binary logistic regression models to assess enduring differences in household (n= 1303) livelihood strategies within VCA villages in 2016. The analysis finds that VCA villages contained significantly more forest loss than the most similar non-VCA villages outside the national park, and greater payments predict increased forest loss in the post-project period. In addition, farming high-value tree crops and cultivating private land were the most important attributes for modeling VCA affiliation among randomly selected households. These results demonstrate that, after payments ceased, project failures increased in severity over time.Those who design and implement conservation-based payments bear great responsibility to ensure their projects are informed by local voice, align with community preferences, and provide sufficient benefits, lest they result in a conservation legacy of increased failure.

    

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3. Using Community Science to Address Pollution in an Urban Watershed: Lessons about Trash, Diverse Engagement, and the Need for Science Mindsets

   https://doi.org/10.1111/j.1936-704X.2021.3359.x  

   Talley, Theresa Sinicrope ; Ruzic, Roxanne ; McKay, Lindsay Goodwin ; Venuti, Nina ; Mothokakobo, Rochelle ( December 2021 , Journal of Contemporary Water Research & Education)

   Community science projects offered in urban areas may be particularly effective at addressing environmental problems and engaging people in science, especially individuals whose identities have historically been underrepresented in the field. In this project, we worked with individuals from a racially diverse, low‐income community in San Diego, California to conduct community science to: 1) test a conceptual program model aimed at engaging diverse communities in science, and 2) contribute to scientific knowledge about the inputs and accumulations of trash in an urban watershed. While the program model did well at bolstering environmental stewardship, recruitment, and short‐term retention of community members as project participants, it was not as effective at building science understanding, interest in science, and awareness of doing science, indicating a need for a mindset approach. Despite this, the data collected by the community between 2014–2018 revealed in‐depth information about the spatial and temporal distributions of trash, including the identification of three main debris inputs: encampments, illegal dumping, and storm drain flows, as well as the validation of global trends of a predominance of plastics across waterways and through time. In a few instances, community stewards became community scientists—the quantity and quality of data collected improved, and community members presented results to authorities who responded with concordant management actions (e.g., help with cleanups, outreach to unhoused communities). Based on project outcomes, our revised community science program model includes a focus on strengthening a science mindset, in which even short‐term science interventions that improve the recognition of science, a sense of belonging, and access to mentorship may have meaningful long‐lasting effects on increased participation in science.

    

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4. Remote-Region Interior Alaska Community Survey with Two Statistical Measures: Arctic Indigenous Entrepreneurial Readiness (ER); and Remote-Region Digital Technology Needs and Skills (DT).

   https://doi.org/10.18739/A20R9M54S  

   Pasch, Timothy ; Kuhlke, Olaf ; Linton, Renee ( January 2021 , Arctic Data Center)

   Between 2018 and 2021 PIs for National Science Foundation Awards # 1758781 and 1758814 EAGER: Collaborative Research: Developing and Testing an Incubator for Digital Entrepreneurship in Remote Communities, in partnership with the Tanana Chiefs Conference, the traditional tribal consortium of the 42 villages of Interior Alaska, jointly developed and conducted large-scale digital and in-person surveys of multiple Alaskan interior communities. The survey was distributed via a combination of in-person paper surveys, digital surveys, social media links, verbal in-person interviews and telephone-based responses. Analysis of this measure using SAS demonstrated the statistically significant need for enhanced digital infrastructure and reworked digital entrepreneurial and technological education in the Tanana Chiefs Conference region. 1. Two statistical measures were created during this research: Entrepreneurial Readiness (ER) and Digital Technology needs and skills (DT), both of which showed high measures of internal consistency (.89, .81). 2. The measures revealed entrepreneurial readiness challenges and evidence of specific addressable barriers that are currently preventing (serving as hindrances) to regional digital economic activity. The survey data showed statistically significant correlation with the mixed-methodological in-person focus groups and interview research conducted by the PIs and TCC collaborators in Hughes and Huslia, AK, which further corroborated stated barriers to entrepreneurship development in the region. 3. Data generated by the survey and fieldwork is maintained by the Tanana Chiefs Conference under data sovereignty agreements. The survey and focus group data contains aggregated statistical/empirical data as well as qualitative/subjective detail that runs the risk of becoming personally identifiable especially due to (but not limited to) to concerns with exceedingly small Arctic community population sizes. 4. This metadata is being provided in order to serve as a record of the data collection and analysis conducted, and also to share some high-level findings that, while revealing no personal information, may be helpful for policymaking, regional planning and efforts towards educational curricular development and infrastructural investment. The sample demographics consist of 272 women, 79 men, and 4 with gender not indicated as a response. Barriers to Entrepreneurial Readiness were a component of the measure. Lack of education is the #1 barrier, followed closely by lack of access to childcare. Among women who participated in the survey measure, 30% with 2 or more children report lack of childcare to be a significant barrier to entrepreneurial and small business activity. For entrepreneurial readiness and digital economy, the scales perform well from a psychometric standpoint. The summary scores are roughly normally distributed. Cronbach’s alphas are greater than 0.80 for both. They are moderately correlated with each other (r = 0.48, p &amp;amp;lt; .0001). Men and women do not differ significantly on either measure. Education is significantly related to the digital economy measure. The detail provided in the survey related to educational needs enabled optimized development of the Incubator for Digital Entrepreneurship in Remote Communities. Enhanced digital entrepreneurship training with clear cultural linkages to traditions and community needs, along with additional childcare opportunities are two among several specific recommendations provided to the TCC. The project PIs are working closely with the TCC administration and community members related to elements of culturally-aligned curricular development that respects data tribal sovereignty, local data management protocols, data anonymity and adherence to human subjects (IRB) protocols. While the survey data is currently embargoed and unable to be submitted publicly for reasons of anonymity, the project PIs are working with the NSF Arctic Data Center towards determining pathways for sharing personally-protected data with the larger scientific community. These approaches may consist of aggregating and digitally anonymizing sensitive data in ways that cannot be de-aggregated and that meet agency and scientific community needs (while also fully respecting and protecting participants’ rights and personal privacy). At present the data sensitivity protocols are not yet adapted to TCC requirements and the datasets will remain in their care. 

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5. A Multi-match Approach to the Author Uncertainty Problem

   https://doi.org/10.2478/jdis-2019-0006  

   Carley, Stephen F. ; Porter, Alan L. ; Youtie, Jan L. ( June 2019 , Journal of Data and Information Science)

   Abstract Purpose The ability to identify the scholarship of individual authors is essential for performance evaluation. A number of factors hinder this endeavor. Common and similarly spelled surnames make it difficult to isolate the scholarship of individual authors indexed on large databases. Variations in name spelling of individual scholars further complicates matters. Common family names in scientific powerhouses like China make it problematic to distinguish between authors possessing ubiquitous and/or anglicized surnames (as well as the same or similar first names). The assignment of unique author identifiers provides a major step toward resolving these difficulties. We maintain, however, that in and of themselves, author identifiers are not sufficient to fully address the author uncertainty problem. In this study we build on the author identifier approach by considering commonalities in fielded data between authors containing the same surname and first initial of their first name. We illustrate our approach using three case studies. Design/methodology/approach The approach we advance in this study is based on commonalities among fielded data in search results. We cast a broad initial net—i.e., a Web of Science (WOS) search for a given author’s last name, followed by a comma, followed by the first initial of his or her first name (e.g., a search for ‘John Doe’ would assume the form: ‘Doe, J’). Results for this search typically contain all of the scholarship legitimately belonging to this author in the given database (i.e., all of his or her true positives), along with a large amount of noise, or scholarship not belonging to this author (i.e., a large number of false positives). From this corpus we proceed to iteratively weed out false positives and retain true positives. Author identifiers provide a good starting point—e.g., if ‘Doe, J’ and ‘Doe, John’ share the same author identifier, this would be sufficient for us to conclude these are one and the same individual. We find email addresses similarly adequate—e.g., if two author names which share the same surname and same first initial have an email address in common, we conclude these authors are the same person. Author identifier and email address data is not always available, however. When this occurs, other fields are used to address the author uncertainty problem. Commonalities among author data other than unique identifiers and email addresses is less conclusive for name consolidation purposes. For example, if ‘Doe, John’ and ‘Doe, J’ have an affiliation in common, do we conclude that these names belong the same person? They may or may not; affiliations have employed two or more faculty members sharing the same last and first initial. Similarly, it’s conceivable that two individuals with the same last name and first initial publish in the same journal, publish with the same co-authors, and/or cite the same references. Should we then ignore commonalities among these fields and conclude they’re too imprecise for name consolidation purposes? It is our position that such commonalities are indeed valuable for addressing the author uncertainty problem, but more so when used in combination. Our approach makes use of automation as well as manual inspection, relying initially on author identifiers, then commonalities among fielded data other than author identifiers, and finally manual verification. To achieve name consolidation independent of author identifier matches, we have developed a procedure that is used with bibliometric software called VantagePoint (see www.thevantagepoint.com) While the application of our technique does not exclusively depend on VantagePoint, it is the software we find most efficient in this study. The script we developed to implement this procedure is designed to implement our name disambiguation procedure in a way that significantly reduces manual effort on the user’s part. Those who seek to replicate our procedure independent of VantagePoint can do so by manually following the method we outline, but we note that the manual application of our procedure takes a significant amount of time and effort, especially when working with larger datasets. Our script begins by prompting the user for a surname and a first initial (for any author of interest). It then prompts the user to select a WOS field on which to consolidate author names. After this the user is prompted to point to the name of the authors field, and finally asked to identify a specific author name (referred to by the script as the primary author) within this field whom the user knows to be a true positive (a suggested approach is to point to an author name associated with one of the records that has the author’s ORCID iD or email address attached to it). The script proceeds to identify and combine all author names sharing the primary author’s surname and first initial of his or her first name who share commonalities in the WOS field on which the user was prompted to consolidate author names. This typically results in significant reduction in the initial dataset size. After the procedure completes the user is usually left with a much smaller (and more manageable) dataset to manually inspect (and/or apply additional name disambiguation techniques to). Research limitations Match field coverage can be an issue. When field coverage is paltry dataset reduction is not as significant, which results in more manual inspection on the user’s part. Our procedure doesn’t lend itself to scholars who have had a legal family name change (after marriage, for example). Moreover, the technique we advance is (sometimes, but not always) likely to have a difficult time dealing with scholars who have changed careers or fields dramatically, as well as scholars whose work is highly interdisciplinary. Practical implications The procedure we advance has the ability to save a significant amount of time and effort for individuals engaged in name disambiguation research, especially when the name under consideration is a more common family name. It is more effective when match field coverage is high and a number of match fields exist. Originality/value Once again, the procedure we advance has the ability to save a significant amount of time and effort for individuals engaged in name disambiguation research. It combines preexisting with more recent approaches, harnessing the benefits of both. Findings Our study applies the name disambiguation procedure we advance to three case studies. Ideal match fields are not the same for each of our case studies. We find that match field effectiveness is in large part a function of field coverage. Comparing original dataset size, the timeframe analyzed for each case study is not the same, nor are the subject areas in which they publish. Our procedure is more effective when applied to our third case study, both in terms of list reduction and 100% retention of true positives. We attribute this to excellent match field coverage, and especially in more specific match fields, as well as having a more modest/manageable number of publications. While machine learning is considered authoritative by many, we do not see it as practical or replicable. The procedure advanced herein is both practical, replicable and relatively user friendly. It might be categorized into a space between ORCID and machine learning. Machine learning approaches typically look for commonalities among citation data, which is not always available, structured or easy to work with. The procedure we advance is intended to be applied across numerous fields in a dataset of interest (e.g. emails, coauthors, affiliations, etc.), resulting in multiple rounds of reduction. Results indicate that effective match fields include author identifiers, emails, source titles, co-authors and ISSNs. While the script we present is not likely to result in a dataset consisting solely of true positives (at least for more common surnames), it does significantly reduce manual effort on the user’s part. Dataset reduction (after our procedure is applied) is in large part a function of (a) field availability and (b) field coverage. 

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