Reviews the effects of gestures, prosody, gaze, and interpersonal space on conversation memory.

The notion of original pronunciation (OP) has arisen because of interest from people who are not themselves phonologists, but who want to know how an earlier period of English sounded to add a fresh dimension to spoken or sung performance. After a discussion of the evidence available at different periods, the paper focuses on Early Modern English, reviewing five constituencies: early music, Bible translations and liturgy, heritage projects, non-dramatic poetry and (especially Shakespearean) theatre. The ways OP has been used by practitioners are described with particular reference to rhyme, wordplay, phonaesthetics and characterisation. A brief review of the history of the OP movement is followed by an illustration of the challenges of working with OP, using a case study of the options surrounding the phonetic character of /r/. Two recent projects, on Keats and Richard III, are summarised. The chapter concludes with a discussion of the extent to which OP projects can achieve authenticity.

A published report should include a sufficient explanation of the statistical methods so that someone with access to the original data could reproduce the reported results. Generally, it is best to divide the methods section of your paper into how subjects were enrolled (Subjects), what interventions were used or how data were acquired (Procedures), how the variables were coded (Measures), and how the data were analyzed (Statistical analysis). Unless there is no missing data, it is important to report the n for each analysis.

What results to report in your paper will vary based on your research question, your analysis, and the style of the journal. In general, for multiple linear regression models, report the regression coefficients, the standard errors of the coefficients, and the statistical significance levels of the coefficients. For logistic regression, report the odds ratio and the 95% confidence interval; for proportional hazards regression, report the relative hazard and the 95% confidence interval.

A valid model is one whether the inferences drawn from it are true. Many factors can threaten the validity of a model including imprecise or inaccurate measurements, bias in study design or in sampling, and misspecification of the model itself.

A key way to validate a model is to replicate the findings with new data. The best method of replication is collecting new data. However, when that is not possible, it is possible to perform a replicate by dividing the sample using a split-group, jackknife, or bootstrap method. Of these 3 methods, split-group is the strongest but requires a dataset large enough to split your sample. A bootstrap is the weakest method of replication, but produces more valid confidence intervals than a simple model.

Multivariable techniques produce two major kinds of information: Information about how well the model (all the independent variables together) fit the data and information about the relationship of each of the independent variables to the outcome (with adjustment for all other independent variables in the analysis). Common measures of the strength of the relationship between an independent variable and the outcome are odds ratio, relative hazard, and relative risk. Adjusting for multiple comparisons is challenging; most important, is to decide ahead of time whether there will be adjustments of multiple comparisons. A common convention is to not adjust the primary outcome, but to adjust secondary outcomes for multiple comparisons.

Your physical state communicates a lot about you. For, example your heart rate and skin conductance can indicate whether you are in fear. This chapter demonstrates how innovations in hardware and sensor technologies allow us to take physiological measurements that can reflect your cognitive state. The chapter discusses readily available sensors in popular devices like smartwatches and phones that can be used to collect physiological data. We then describe what each sensor – accelerometers, GPS, thermometers, heart rate monitors, and their combination – can reveal about the mind. The chapter also provides advice on how to analyze such richly sampled data, and we discuss privacy concerns that can come with such deep data collection.

The strength of multivariable analysis is its ability to determine how multiple independent variables, which are related to one another, are related to an outcome. However, if two variables are so closely correlated that if you know the value for one variable you know the value of the other, multivariable analysis cannot separately assess the impact of these two variables on outcome. This problem is called multicollinearity.

To assess whether there is multicollinearity, investigators should first run a correlation matrix. However, the matrix only tells you the relationship between any two independent variables. Harder to detect is whether a combination of variables accounts for another variable’s value. Two related measure of muticollinearity are tolerance and the reciprocal of tolerance: the variation inflation factor. If you have variables that are highly related, you can omit one or more of the variables, use an “and/or” clause or create a scale.

The 1991 Gulf War placed Israel in a unique strategic position as Iraqi missiles targeted its cities while the US urged it not to retaliate. Saddam Hussein’s goal was to fracture the US-led coalition, but Prime Minister Yitzhak Shamir showed restraint at President Bush’s request. This chapter examines the US-Israel dynamic during the war, focusing on intelligence sharing, the deployment of Patriot missile batteries, and Israeli debates over military response. Despite US assurances to strike Iraqi launch sites, tensions persisted over arms sales to Arab states and Israel’s strategic concerns. The war also intensified political strain, particularly around US loan guarantees. While Israel sought help to absorb Soviet Jewish immigrants, the Bush administration tied financial support to a freeze on settlement expansion. These developments reflected broader shifts in US-Israel relations, where strategic alignment coexisted with policy disagreements. By analyzing these interactions, the chapter sheds light on how military threats, diplomacy, and aid negotiations shaped the relationship during and after the Gulf War.

Sensitivity analysis tests how robust the results are to changes in the underlying assumptions of your analysis. In other words, if you made plausible changes in your assumptions, would you still draw the same conclusions? The changes could be a more restrictive or inclusive sample, a different way to measure your variables, a different way for handling missing data, or a change of a different feature of your analysis. With sensitivity analysis you cannot lose. If you vary the assumptions of your analysis and you get the same result, you will have more confidence in the conclusions of your study. Conversely, if plausible changes in your assumptions lead to a different conclusion, you will have learned something important. A common assumption tested in sensitivity analysis is that there are no unmeasured confounders, which can be tested with E values or falsification analysis. Other common assumptions tested are that losses to follow-up are random, that the sample is unbiased, that there is the correct exposure period and follow-up period, that there is a biased predictor or outcome, or that the model is misspecified.

Orfeas Chasapis TassinisWhat law should apply to contracts concluded between international organizations and private parties? Probing the concept of a ‘right’ to party autonomy, this chapter employs a Holfeldian framework to unpack the perspectival dimension of this age-old problem. It argues that international organizations may be at liberty of choosing the law applicable to their contracts, but domestic legal orders are not necessarily under an obligation to recognize that choice as effective. Arguably, however, deference to party choice is due in the context of arbitration. Yet, the frequent absence of party choice puts pressure on arbitrators to make principled choices on applicable law. Given the lack of clarity on how these choices are supposed to be made, the ‘closest connection’ test is put forward as perhaps the best safeguard for objectivity and predictability.

Chapter 1 describes the use of Arthurian material in English political thought alongside documentary practices that attempt to construct an empire which includes Scotland, beginning with the Scottish succession crisis (1286–92) and extending to the Wars of the Roses. These practices had lasting effects, as their citation of legendary figures such as King Arthur opened an abundance of chronicle and romance material to argument. Historiographical and literary texts such as John Hardyng’s Chronicle, the Alliterative Morte Arthure, the Awntyrs off Arthur, and Thomas Malory’s Le Morte Darthur responded to this invocation of real and legendary history. While some simply repeat and extend governmental aspirations, others, such as the Morte Arthure and Awntyrs, question English imperial kingship and disrupt acts of sovereign recognition through recognition scenes. Ultimately, however, no medieval English author could imagine an alternative to the antagonism of sovereignty discourse, highlighting the problematic relationship of politics to precedent.

Chapter 3 reflects upon the sonic landscape within the opera. In a romantic opera, the orchestra is the cementing agent between the separate dramatic forces of the text and the voice as it often serves as an omniscient narrator, detailing to the listener not only the actions of the characters on stage and the external environment but also the unspoken and unseen world of internal thoughts and desires. By listening to the orchestra, the audience can interpret the sounds they hear as reflective of the overall goals of the principal characters on stage. For example, the ominous use of pianissimo timpani paired with bass drum at the start of the orchestral prelude foreshadows for the listener the death of Lucia, as this is the same music heard in the opera’s final scene. The opening sounds of the prelude remind us that this work is indeed a tragedy where death is the prescribed outcome not only for Lucia but also for her lover, Edgardo. Of particular interest in this chapter is the glass harmonica, originally planned by Donizetti to be used in the ‘mad scene’ of Act III but later replaced with the flute. Donizetti’s original intent of using this high-pitched resonant instrument to depict female madness has come back into practice in modern productions of the work. This presents audiences with contemporaneous sounds of horror, violence and mystery commonly found in fantasy and sci-fi films today.