Mobilizing master narratives through categorical narratives and categorical statements when default identities are at stake

Abstract

In research interviews, interviewees are usually well aware of why they were selected, and in their narratives they often construct ‘default identities’ in line with the interviewers’ expectations. Furthermore, narrators draw on shared cultural knowledge and master narratives that tend to form an implicit backdrop of their stories. Yet in this article we focus on how some of these master narratives may be mobilized explicitly when default identities are at stake. In particular, we investigate interviews with successful female professionals from diverse geographical contexts. We found that the interviewees deal with challenges to their ‘successful professional’ identities by drawing on categorical narratives or categorical statements. As such, the interviewees talk into being a morally ordered gendered worldview, thus making explicit gendered master narratives about their societies and workplaces. In general, this article shows that categorical narratives and statements can bring (the typically rather elusive) master narratives to the surface and that these can thus contribute to the narrators’ identity work.

The Narrative Dimensions Model and an exploration of various narrative genres

“…By means of this model, it is possible to pinpoint the particular similarities as well as the differences between individual narratives, which could otherwise be hidden by the categorization within one or more genres. Furthermore, people may mix a number of narrative genres (e.g., a partially generic, partially habitual narrative of vicarious experience (for an example, see Clifton et al, 2020, p. 57)) or may sometimes shift from one genre to another while telling their story (for an example, see Van De Mieroop et al, 2017). By scrutinizing such narratives and the shifts within them via the Narrative Dimensions Model, researchers may really bring to the fore the intricate, usually non-arbitrary, multi-dimensional nature of narratives (for examples, see Van De Mieroop, forthcoming).…”

“…Membership Categorization Analysis, see Sacks, 1986). For example, a woman may talk about her experiences with sexism at work from a collective perspective, viz., in terms of what happens to women in general in the workplace (see Van De Mieroop et al, 2017). Furthermore, narrators may also tell stories about others, e.g., when "scripting the lines" of a narrative of vicarious experience, when acting as a "talking machine" and animating a traditional, pre-scripted narrative or when performing any role in-between.…”

Narrative and gender as mutually constituted meaning-making systems

“…For example, events that are assumed to be part of the typical life course, such as meeting and committing to a romantic partner, are gendered in the particulars of how these events stereotypically unfold differently for women and men (Ottsen and Berntsen 2014). Even non-gender specific life experiences, such as personal challenges or turning points, are expressed in gendered stereotyped ways; for example, challenging experiences are resolved through personal strength and autonomy for males but through communal caring and relationships for females (Thorne and McLean 2003), sad experiences are resolved through apologies and re-establishing the relationship for females, but not so much for males (Adams et al 1995), and women who are highly successful in stereotypically male professions provide narratives that contextualise their professional lives in terms of family relationships and obligations (Van De Mieroop et al 2017).…”

Sentiment-Target Word Pair Extraction Model Using Statistical Analysis of Sentence Structures

“…The words processed in this way were classified into positive, negative, or neutral sentiments [7]. All of the applicable models are subject to issues in either the PMI method or in a method that applies rules after morpheme analysis: For the PMI method, words that are either high or low in frequency; for methods that apply rules, sentences that fall outside the categorized rule statements [15,16]. The issues stem from relying on simple probability information while the syntactic structures of sentences are not identified as of yet, or from trying to express everything with rules that solve the syntactic structures of sentences manually.…”