The heat capacities of glassy and liquid toluene and ethylbenzene were measured with an adiabatic calorimeter. Both samples were doped with about 10% of benzene to suppress crystallization. The effects of the doping were corrected for by assuming the additivity of the heat capacities of toluene (or ethylbenzene) and benzene. The configurational entropies of several glass-forming liquids, including toluene and ethylbenzene, were calculated as functions of temperature from their heat-capacity data. For these calculations, the vibrational heat capacities were determined by the least-squares fitting of the Debye and Einstein functions to the experimental values using auxiliary spectroscopic data from the literature. The size of cooperative rearranging region (CRR), which was first conceived by Adam and Gibbs, was calculated from the configurational entropy in a simplifying approximation that neglects distribution of CRR size and internal entropy of CRR. For all of the systems examined, the size of CRR increases with decreasing temperature and is frozen-in at four to eight molecules per region at the glass-transition temperature.

In the social sciences, many quantitative research findings as well as presentations of demographics are related to participants' gender. Most often, gender is represented by a dichotomous variable with the possible responses of woman/man or female/male, although gender is not a binary variable. It is, however, rarely defined what is meant by gender. In this article, we deconstruct the concept 'gender' as consisting of several facets, and argue that the researcher needs to identify relevant aspects of gender in relation to their research question. We make a thorough exposition of considerations that the researcher should bear in mind when formulating questions about each facet, in order to exemplify how complex this construct is. We also remind the researcher that gender is not a binary category and discuss challenges in the balance between taking existing gender diversity into account and yet sorting participants into gender categorisations that function in statistical analyzes. To aid in this process, we provide an empirical example on how gender identity may be categorised when using a free-text response. Lastly, we suggest that other measurements than participants' gender might be better predictors of the outcome variable.

The implementation of gender fair language is often associated with negative reactions and hostile attacks on people who propose a change. This was also the case in Sweden in 2012 when a third gender-neutral pronoun hen was proposed as an addition to the already existing Swedish pronouns for she (hon) and he (han). The pronoun hen can be used both generically, when gender is unknown or irrelevant, and as a transgender pronoun for people who categorize themselves outside the gender dichotomy. In this article we review the process from 2012 to 2015. No other language has so far added a third gender-neutral pronoun, existing parallel with two gendered pronouns, that actually have reached the broader population of language users. This makes the situation in Sweden unique. We present data on attitudes toward hen during the past 4 years and analyze how time is associated with the attitudes in the process of introducing hen to the Swedish language. In 2012 the majority of the Swedish population was negative to the word, but already in 2014 there was a significant shift to more positive attitudes. Time was one of the strongest predictors for attitudes also when other relevant factors were controlled for. The actual use of the word also increased, although to a lesser extent than the attitudes shifted. We conclude that new words challenging the binary gender system evoke hostile and negative reactions, but also that attitudes can normalize rather quickly. We see this finding very positive and hope it could motivate language amendments and initiatives for gender-fair language, although the first responses may be negative.

Different strategies of gender-fair language have been applied to reduce a male bias, which means the implicit belief that a word describing an undefined person describes a man. This male bias might be caused by the words themselves in terms of generic masculine or masculine forms or by androcentrism (the conflation of men with humanity). In two experiments, we tested how different gender-fair strategies used as labels of an unknown social target (an applicant in a recruitment situation) could eliminate the male bias. The three types of gender-fair strategies tested were: (a) paired forms (he/she), (b) traditional neutral words (e.g., singular they, Bthe applicant^), or (c) gender-neutral third-person pronouns actively created to challenge the binary gender system (ze, Swedish hen). The two experiments were performed in Swedish with 417 undergraduates in Sweden and in English with 411 U.S. participants recruited online. In Swedish, the third-person gender-neutral pronoun singular (hen) was used. In English, several forms of such gender-neutral pronouns have been suggested (e.g., ze). In both experiments, results indicated that paired forms and actively created gender-neutral pronouns eliminated the male bias, whereas traditional neutral words contained a male bias. Thus, gender-fair language strategies should avoid using traditional words. Consequences of using paired forms and creating new gender-neutral words are discussed. We argue that an actively created gender-neutral pronoun is of highest value because it is more inclusive.