The output of predictive models is routinely recalibrated by reconciling low-level predictions with known quantities defined at higher levels of aggregation. For example, models predicting vote probabilities at the individual level in U.S. elections can be adjusted so that their aggregation matches the observed vote totals in each county, thus producing better-calibrated predictions. In this research note, we provide theoretical grounding for one of the most commonly used recalibration strategies, known colloquially as the “logit shift.” Typically cast as a heuristic adjustment strategy (whereby a constant correction on the logit scale is found, such that aggregated predictions match target totals), we show that the logit shift offers a fast and accurate approximation to a principled, but computationally impractical adjustment strategy: computing the posterior prediction probabilities, conditional on the observed totals. After deriving analytical bounds on the quality of the approximation, we illustrate its accuracy using Monte Carlo simulations. We also discuss scenarios in which the logit shift is less effective at recalibrating predictions: when the target totals are defined only for highly heterogeneous populations, and when the original predictions correctly capture the mean of true individual probabilities, but fail to capture the shape of their distribution.

Three experiments (N = 550) examined the effect of an interval construction elicitation method used in several expert elicitation studies on judgment accuracy. Participants made judgments about topics that were either searchable or unsearchable online using one of two order variations of the interval construction procedure. One group of participants provided their best judgment (one step) prior to constructing an interval (i.e., lower bound, upper bound, and a confidence rating that the correct value fell in the range provided), whereas another group of participants provided their best judgment last, after the three-step confidence interval was constructed. The overall effect of this elicitation method was not significant in 8 out of 9 univariate tests. Moreover, the calibration of confidence intervals was not affected by elicitation order. The findings warrant skepticism regarding the benefit of prior confidence interval construction for improving judgment accuracy.

Individuals often assess themselves as being less susceptible to common biases compared to others. This bias blind spot (BBS) is thought to represent a metacognitive error. In this research, we tested three explanations for the effect: The cognitive sophistication hypothesis posits that individuals who display the BBS more strongly are actually less biased than others. The introspection bias hypothesis posits that the BBS occurs because people rely on introspection more when assessing themselves compared to others. The conversational processes hypothesis posits that the effect is largely a consequence of the pragmatic aspects of the experimental situation rather than true metacognitive error. In two experiments (N = 1057) examining 18 social/motivational and cognitive biases, there was strong evidence of the BBS. Among the three hypotheses examined, the conversational processes hypothesis attracted the greatest support, thus raising questions about the extent to which the BBS is a metacognitive effect.

Do people cheat more when they have something to gain, or when they have something to lose? The answer to this question isn’t straightforward, as research is mixed when it comes to understanding how unethical people will be when they might acquire something good versus avoid something bad. To wit, research has found that people cheat more in a loss (vs. gain) frame, yet research on regulatory focus has found that people cheat more in a promotion focus (where the focus is on acquiring gains) than in a prevention focus (where the focus is on avoiding losses). Through a large-scale field study containing 332,239 observations including 27,350 transgressions, we address the contradictory results of gain/loss frames and regulatory focus on committing unethical behavior in a context that contains a high risk of detecting unethical behavior (NFL football games). Our results replicated the separate effects of more cheating in a loss frame, and more cheating in a promotion focus. Furthermore, our data revealed a heretofore undocumented crossover interaction, in accordance with regulatory fit, which could disentangle past results: Specifically, we found promotion focus is associated with more cheating in a loss (vs. gain) frame, whereas prevention focus is associated with more cheating in a gain (vs. loss) frame. In gridiron football, this translates to offensive players fouling more when their team is losing (vs. winning) and defensive players fouling more when their team is winning (vs. losing).

Farm animal welfare has become an important issue for the European public, especially in the last two decades when a number of crises (eg Bovine Spongiform Encephalopathy and Avian Influenza) have affected farm animal populations. Public concern about this issue led the European Union to fund the Welfare Quality® project. This project aimed to develop a protocol for assessing animal welfare on farms and at slaughter plants, to identify the main animal welfare problems, and to address possible welfare improvement strategies. In fulfilling these aims, the Welfare Quality® project incorporated inputs from both science and society. This was crucial, as the public perception of what constitutes ‘animal welfare’ sometimes differs from animal science-based definitions. Furthermore, these differences are often interwoven with broader variations in ethical- and value-based understandings about human/non-human animal relationships. This paper presents the steps that we adopted to establish a dialogue between science and society during the construction of the Welfare Quality® assessment protocols. This dialogue involved numerous interactions between animal scientists, social scientists and members of the public. These interactions took several forms, including: meetings, conferences, workshops, websites, newsletters, interviews, focus groups, and citizen and farmers juries. Here, we address four key moments within this dialogue: the development of the initial list of twelve welfare criteria; the consumer focus groups; the development of the Welfare Quality® scoring system; and the citizen juries. In particular, we focus on the results of the focus groups and citizen juries. The focus groups were conducted in France, Italy, Sweden, The Netherlands, the United Kingdom, Norway, and Hungary and the citizen juries were carried out in Italy, the United Kingdom, and Norway. Drawing on this research, we highlight the similarities and differences between societal understandings of farm animal welfare and the views of scientific experts. Furthermore, and crucially, we outline how the animal scientists took account of societal opinion when developing their farm animal welfare assessment tools.

Despite the adversity presented by COVID-19 pandemic, it also pushed for experimenting with innovative strategies for community engagement. The Community Research Advisory Council (C-RAC) at Johns Hopkins University (JHU), is an initiative to promote community engagement in research. COVID-19 rendered it impossible for C-RAC to conduct its meetings all of which have historically been in person. We describe the experience of advancing the work of the C-RAC during COVID-19 using digital and virtual strategies. Since March 2020, C-RAC transitioned from in person to virtual meetings. The needs assessment was conducted among C-RAC members, and individualized solutions provided for a successful virtual engagement. The usual working schedule was altered to respond to COVID-19 and promote community engaged research. Attendance to C-RAC meetings before and after the transition to virtual operation increased from 69% to 76% among C-RAC members from the community. In addition, the C-RAC launched new initiatives and in eighteen months since January 2020, it conducted 50 highly rated research reviews for 20 research teams. The experience of the C-RAC demonstrates that when community needs are assessed and addressed, and technical support is provided, digital strategies can lead to greater community collaborations.

The coronavirus disease 2019 (COVID-19) pandemic has placed significant burden on healthcare systems. We compared Clostridioides difficile infection (CDI) epidemiology before and during the pandemic across 71 hospitals participating in the Canadian Nosocomial Infection Surveillance Program. Using an interrupted time series analysis, we showed that CDI rates significantly increased during the COVID-19 pandemic.

Due to increasing sustainability demands, textiles manufacturing, an industry that uses substantial amounts of natural resources, energy and labour, are facing tough challenges in the years ahead. One of the more overlooked concepts with great potential for sustainable manufacturing is Industry 4.0. This paper addresses how the textile industry is engaging with Industry 4.0 technologies and applications in the context of sustainable manufacturing. A proposal for an implementation framework is introduced based on a literature review within this field.

Kozłowskiite, ideally Ca4(Fe2+Sn3)(Si2O7)2(Si2O6OH)2, is a new mineral isostructural with kristiansenite and silesiaite, found as a band in the core of a zoned silesiaite–kristiansenite crystal from the granitic pegmatite at Szklarska Poręba, Lower Silesia, Poland. In tiny pieces kozłowskiite is pale brownish, with a calculated density of 3.775 g⋅cm–3 and a mean refractive index ~1.727. The triclinic crystal structure was determined with space-group symmetry C1: a = 10.0183(2), b = 8.3861(1), c = 13.3395(2) Å, α = 89.956(1), β = 109.039(2), γ = 89.979(1)° and V = 1059.40(3) Å3, although, similarly to kristiansenite, it is metrically monoclinic (Laue group 2/m) with α and γ angles equal to 90° and a = 10.0170(3), b = 8.3860(2), c = 13.3421(4) Å, β = 109.050(3)° and V = 1059.40(5) Å3. The seven strongest reflections in the calculated powder X-ray diffraction pattern are [d in Å (I) hkl]: 5.190 (73.2) 111, 1$\bar{1}$1; 4.569 (30.0) $\bar{2}$02; 3.153 (64.7) 004; 3.094 (28.1) $\bar{3}$11, $\bar{3}\bar{1}$1; 3.089 (100) $\bar{2}\bar{2}$2, $\bar{2}$22; 2.595 (27.2) 2$\bar{2}$2, 222; and 2.141 (30.6) $\bar{3}$31, $\bar{3}\bar{3}$1. Electron microprobe analysis gave (in wt.%) SiO2 39.46, ZrO2 0.35, SnO2 31.13, Al2O3 0.35, Sc2O3 2.65, total Fe as Fe2O3 5.06 (= Fe2O3calc. 2.26; FeOcalc. 2.52), MnO 0.71, CaO 18.42 and H2Ocalc. 1.48, sum 99.33. The empirical formula on the basis of 26O + 2(OH) and 16 cations (Z = 2) is Ca4.00(Sn2.52Sc0.47Fe2+0.43Fe3+0.34Mn2+0.12Al0.08Zr0.03)Σ4.00(Si2.00O7)2(Si2.00O6OH)2. The crystal structure of kozłowskiite was refined to an R1 = 2.12% for 4887 reflections with Io>2σI. The Ca and Si sites are occupied solely by Ca and Si, respectively, and three of four M sites: M2, M3 and M4, are dominated by Sn. Four hydrogen atoms present in the kozłowskiite unit cell are shared among the O17–O27 and O47–O37 oxygen atoms where O17 and O47 are OH groups forming relatively strong hydrogen bonds.

A comparison of computer-extracted and facility-reported counts of hospitalized coronavirus disease 2019 (COVID-19) patients for public health reporting at 36 hospitals revealed 42% of days with matching counts between the data sources. Miscategorization of suspect cases was a primary driver of discordance. Clear reporting definitions and data validation facilitate emerging disease surveillance.