Can “Googling” correct misbelief? Cognitive and affective consequences of online search

With increasing concern over online misinformation in perspective, this study experimentally examined the cognitive as well as the affective consequences of online search. Results of the two experiments using widely shared, prejudiced misinformation about an ethnic minority in Japan indicated that (a) online search reduces on average the likelihood of believing the misinformation, (b) the magnitude of the effect is larger among those who are predisposed to believe the misinformation, (c) cognitive correction is observed whether searchers are motivated to achieve a directional goal or an accuracy goal, and (d) online search deteriorates affective feeling toward the target groups of the misinformation. Theoretical implications are discussed in relation to the robustness of confirmation bias in online search and the “belief echo” in which exposure to negative misinformation continues to shape attitudes even after the misinformation has been effectively discredited.

CALSPEC: HST Spectrophotometric Standards at 0.115 to 32 μm with a 1% Accuracy Goal

The flux distributions of spectrophotometric standard stars were initially derived from the comparison of stars to laboratory sources of known flux but are now based on calculated model atmospheres. For example, pure hydrogen white dwarf (WD) models provide the basis for the HST CALSPEC archive of flux standards. There is good evidence that relative fluxes from the visible to the near-IR wavelength of ∼2.5 μm are currently accurate to ∼1% for the primary reference standards.

Does an Asset Management Firm's Stock Holding Made in Response to Buy-Side Analysts' Prior Recommendations Induce Subsequent Forecast Optimism?

ABSTRACT In this study, we examine whether buy-side analysts' forecasts are biased when they cover stocks that are held by the mutual fund. Specifically, we conduct an experiment with buy-side analysts and fund managers in which we manipulate whether the forecasts are released to colleagues and clients or kept private, and whether the mutual fund has made an investment in the stock based on the recommendation of the analyst. We posit and find evidence that when forecasts are made available to other colleagues and clients, participants' forecasts are more positive when the mutual fund has a holding (versus no holding) in a stock based on the buy-side analysts' recommendations. These forecasts are also more optimistic than a control group where participants have an accuracy goal. This effect disappears when the forecasts are kept private. In a supplementary experiment, we document that the effect is conscious, consistent with a strategic justification mechanism. Data Availability: Contact the authors.

What Observation Scheme Should We Use for Profiling Floats to Achieve the Argo Goal for Salinity Measurement Accuracy? Suggestions from Software Calibration

To meet the Argo Project’s accuracy goal for salinity measurements (±0.01), several measurement schemes for profiling float observations were examined using the standard Argo software calibration for salinity. In this study, salinity calibration errors are evaluated in data series observed by employing several different measurement schemes. In the North Pacific the accuracy goal can be achieved by programming for constant measurements to a depth of 1500 db or more; however, measurements in the mixed water region require the adoption of a scheme that constantly measures salinity profiles to the marginal capability depth of floats (e.g., 2000 db), so that calibration errors can be minimized. In contrast, in the tropical regions of the Pacific and eastern Indian Ocean, 1000-db profiles provide salinity data of sufficient quality. When floats use a Park and Profile scheme, it is recommended that they be programmed to measure deeper profiles every observation cycle. Otherwise, it is impossible to detect and correct suspicious data, such as a salinity jump in which a salinity measurement suddenly differs from previous measurements by ±0.02–0.03 or more.