Does democracy require value-neutral science? Analyzing the legitimacy of scientific information in the political sphere

Philosophers now commonly reject the value free ideal for science by arguing that non-epistemic values, including personal or social values, are permissible within the core of scientific research. However, little attention has been paid to the normative political consequences of this position. This paper explores these consequences and shows how political theory is fruitful for proceeding in a world without value-neutral science. I draw attention to an oft-overlooked argument employed by proponents of the value free ideal I dub the “political legitimacy argument.” This argument claims that the value-free ideal follows directly from the foundational principles of liberal democracy. If so, then the use of value-laden scientific information within democratic decision making would be illegitimate on purely political grounds. Despite highlighting this unaddressed and important argument, I show how it can be rejected. By appealing to deliberative democratic theory, I demonstrate scientific information can be value-laden and politically legitimate. The deliberative democratic account I develop is well suited for capturing the intuitions of many opponents of the value free ideal and points to a new set of questions for those interested in values in science.     

Seven components of judgmental forecasting skill: Implications for research and the improvement of forecasts

A decomposition of the Brier skill score shows that the performance of judgmental forecasts depends on seven components: environmental predictability, fidelity of the information system, match between environment and forecaster, reliability of information acquisition, reliability of information processing, conditional bias, and unconditional bias. These components provide a framework for research on the forecasting process. Selected literature addressing each component is reviewed, and implications for improving judgmental forecasting are discussed.     

Karyopherin-mediated import of integral inner nuclear membrane proteins

Targeting of newly synthesized integral membrane proteins to the appropriate cellular compartment is specified by discrete sequence elements, many of which have been well characterized. An understanding of the signals required to direct integral membrane proteins to the inner nuclear membrane (INM) remains a notable exception. Here we show that integral INM proteins possess basic sequence motifs that resemble 'classical' nuclear localization signals. These sequences can mediate direct binding to karyopherin-alpha and are essential for the passage of integral membrane proteins to the INM. Furthermore, karyopherin-alpha, karyopherin-beta1 and the Ran GTPase cycle are required for INM targeting, underscoring parallels between mechanisms governing the targeting of integral INM proteins and soluble nuclear transport. We also provide evidence that specific nuclear pore complex proteins contribute to this process, suggesting a role for signal-mediated alterations in the nuclear pore complex to allow for passage of INM proteins along the pore membrane.     

The teachers/practitioners corner the effects of indexing ARMA series using the consumer price index

The authors demonstrate that indexing a time series with an ARMA representation using the Consumer Price Index does not materially alter the ARMA form of the model. They further demonstrate that the forecasting error of the indexed series and of the product of the forecasts of the index and the time series are, for practical purpose, the same. Simulation results are reported for five model classes.     

A comparative arima analysis of the 111 series of the makridakis competition

The 111 series of the Makridakis competition are used to address a number of questions pertaining to use of the Box–Jenkins technique. The ARIMA models developed are compared to the ARIMA models developed independently by Andersen for the Makridakis competition. The time required to perform the analysis for each series is discussed in terms of model complexity. Forecast accuracy, measured as the MAPE for the one step ahead forecast, is discussed for different series lengths.     

The worldwide leaf economics spectrum

Bringing together leaf trait data spanning 2,548 species and 175 sites we describe, for the first time at global scale, a universal spectrum of leaf economics consisting of key chemical, structural and physiological properties. The spectrum runs from quick to slow return on investments of nutrients and dry mass in leaves, and operates largely independently of growth form, plant functional type or biome. Categories along the spectrum would, in general, describe leaf economic variation at the global scale better than plant functional types, because functional types overlap substantially in their leaf traits. Overall, modulation of leaf traits and trait relationships by climate is surprisingly modest, although some striking and significant patterns can be seen. Reliable quantification of the leaf economics spectrum and its interaction with climate will prove valuable for modelling nutrient fluxes and vegetation boundaries under changing land-use and climate.     

Computer simulation and the features of novel empirical data

In an attempt to determine the epistemic status of computer simulation results, philosophers of science have recently explored the similarities and differences between computer simulations and experiments. One question that arises is whether and, if so, when, simulation results constitute novel empirical data. It is often supposed that computer simulation results could never be empirical or novel because simulations never interact with their targets, and cannot go beyond their programming. This paper argues against this position by examining whether, and under what conditions, the features of empiricality and novelty could be displayed by computer simulation data. I show that, to the extent that certain familiar measurement results have these features, so can some computer simulation results.     

A case illustrating the importance of adequate model criticism

A case is discussed where a failure to adequately criticize an ARIMA model led to erroneous inferences about the process underlying the data. A follow-up analysis, which permitted model criticism, suggested a different interpretation. The case is suggested for classroom presentation.