Apologizing strategies in Spanish: a diachronic and sociolinguistic approach

This projects fits within the vast body on politeness research, but focuses on one specific politeness strategy in Spanish, namely the speech act of apologizing. Although a large number of studies have already been dedicated to the study of politeness and speech acts in the English-speaking world, it is only until recently that it has emerged in the field of Hispanic studies. The attraction towards this category should not be surprising, since the act of apologizing constitutes a complex speech act that involves an ample gamut of both language-internal and language-external features. In order to account for this multidimensional nature of apologizing, the phenomenon will be studied from a multiple perspective, according to two dimensions of linguistic variation (1) diachronic variation and (2) sociolinguistic variation in contemporary Spanish.

1. The diachronic study will focus on the development of a set of near-synonymous parenthetical apologetic markers lo siento, lo lamento, disculpa/e, perdona/e.  In addition to a detailed overview of the (morpho-) syntactic, semantic and more discursive characteristics of these markers over time, the proposed project also aims at contributing to the field of quantitative historical linguistics by presenting the first empirical assessment of the periodization problem in Spanish.  
2. Apologizing strategies will also be studied from a sociolinguistic angle including age, gender and educational level in colloquial contemporary Spanish. This second part builds on the first, but places these speech acts of regret and apology within the broader framework of politeness research in contemporary Spanish. 

In order to ensure a diversified and well-balanced corpus, our data come from manually annotated data points based on the Spanish Corpus Diacrónico del Español for the diachronic data, and from the Corpus Oral de Madrid and Corpus Oral del Lenguaje Adolescente de Madrid for the contemporary data. These data will be analyzed by means of multifactorial statistical modeling in order to account for the effect of the multiple variables involved and how they relate to each other.