Disallowed identities and what to call them: statistical models for the linguistic analysis of neologisms surrounding perinatal death

Résumé En

Perinatal bereavement is one of the most challenging yet unspoken types of grief. In many European languages the lack of a lexicalised item to refer to prematurely deceased babies and their parents witnesses to the stigmatization that still broadly surrounds perinatal death (Heazell 2016; Markin & Zilcha-Mano 2018), making it difficult to identify, let alone acknowledge, those who have experienced it. The absence of officially recognised terms to designate a bereaved parent for instance, makes their unnamed identity invisible in society and consequently oppressed and ‘disallowed’. In countries like France people are currently petitioning for the recognition of a name (Caliendo/Ruchon 2020) to define those who have suffered what is called the “silent” or “invisible” loss (Lang et al. 2011). In the context of perinatal death, identifying new terms to designate parents and their deceased babies affirms the reality of their bereaved condition, which can prove beneficial to tackle the invisibility of perinatal loss and help prevent cases of dysfunctional grief. As defended by hypostatization theory (Schmid 2008), words have a concept-forming power, in the sense that the existence of a unique term renders what is denoted conceptually real to language users, creating the idea that there is a corresponding tangible entity denoted by that word. Schmid (2008: 3) himself suggests that “The process of hypostatization is temporally prior to and logically necessary for the spreading of a new word”. In the light of the above, this paper seeks to reflect on the issue of designation in the French language and to encourage a naming policy by contributing to the questions that bereaved parents raise in their discourse: how can the bereaved refer to their deceased babies, and how can bereaved mothers and fathers see their identity as parents finally recognised? To address this issue, the collaborative research project PERINAT (https://perinat.univ-lille.fr/), funded by the French National Research Agency in 2023, reflects on the complex nature of perinatal death from a discursive perspective. More specifically, the research project focuses on the use of lexical innovation (neologisms) in the metadiscourse of bereaved parents through an inventory of the self- and baby designating terms they use. The innovative aspect of this work lies in the collaboration between the authors and the actors directly concerned by perinatal death, who are sources of situated viewpoints, with the final aim of contributing to the validation of a designating term that is deemed acceptable. The PERINAT project analyses the use of neologisms by means of questionnaires administered to a total of 85 bereaved parents (38 male, 47 female). Seventeen terms are proposed as possible neologisms to denote bereaved parents and their babies to be rated on a rating scale with five categories ranging from “I refuse to use this word” to “I am already using it”. Two different approaches have been used for the evaluation of these ordinal variables, cumulative models with proportional assumption and latent trait models for ordinal data. The former is based on a single item while the latter on multiple items. Both of them work on modelling uncertainty as a latent component in the response process measuring multiple factors, including knowledge, interest and involvement in the topic. The first model aims at identifying the socio-demographic variables that most influence the choice of one neologism over another. The second model identifies latent traits and examines the quality and correctness of the items used in the survey. The analysis can identify patterns and trends in the responses, as well as factors that influence the acceptance of neologisms among bereaved parents. By understanding these factors, one can identify a neologism that accurately captures the emotional and psychological experience of perinatal bereavement and is therefore more likely to be accepted and used by those affected. Additionally, the statistical models can provide insights into the quality and validity of the survey questions, allowing researchers to refine their methods and improve future studies.