Source Domain Associations as Conceptual Assemblages in Trauma Talk – an Association Rule Mining Approach.
The qualitative nature of co-deployed or "associated" metaphorical source domains in discourse has been extensively researched, often in terms of whether they share common conceptual roots. There are however few empirical studies on the strength of these associations and their implications. In menta...
| Publicado en: | Metaphor & Symbol Vol. 39; no. 2; pp. 96 - 110 |
|---|---|
| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
Taylor & Francis Ltd
Apr-Jun2024
|
| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=176121255&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 176121255 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10926488 7MO jtl: Metaphor & Symbol issn: 10926488 maglogo: N pubinfo: dt: Apr-Jun2024 vid: 39 iid: 2 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 176121255 10.1080/10926488.2023.2300431 ppf: 96 ppct: 14 formats: fmt: – @attributes: type: T db: hlh ui: 176121255 – @attributes: type: P db: hlh ui: 176121255 tig: atl: Source Domain Associations as Conceptual Assemblages in Trauma Talk – an Association Rule Mining Approach. aug: au: Tay, Dennis Qiu, Han affil: The Hong Kong Polytechnic University Department of Philosophy, Linguistics and Theory of Science, Faculty of Humanities, University of Gothenburg su: Hong Kong (China) Social unrest Association rule mining Mental health counseling Machine learning sug: subj: Social unrest Hong Kong (China) Association rule mining Mental health counseling Machine learning ab: The qualitative nature of co-deployed or "associated" metaphorical source domains in discourse has been extensively researched, often in terms of whether they share common conceptual roots. There are however few empirical studies on the strength of these associations and their implications. In mental healthcare activities like psychological interviews and counseling, for example, strongly associated sources may suggest unique "conceptual assemblages" that highlight underexplored (dis)similarities between clients or client groups, beyond the typical focus on isolated sources and frequencies. Our case study compares source domain associations produced by two interviewee groups – one meeting the diagnostic threshold for acute stress disorder (ASD), the other not – relating their first-hand experiences of traumatic events during the 2019 Hong Kong social unrest. The machine learning method of association rule mining is used to extract and quantify the top association rules in both groups. Results show i) a shared rule suggesting a highly schematic construal of trauma that nevertheless varies in instantiating details, and ii) several distinct rules that corroborates and lends further insight into the "agentive vs. non-agentive" characteristics exhibited by ASD vs. non-ASD individuals. Implications and future directions, including potential extensions to other discourse contexts, are discussed. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
|---|