MSDIP: A Method for Coding Source Domains in Metaphor Analysis

Figures & data

Figure 1. The coding scheme for metaphorical source domain identification.

Table 1. Overview of intercoder reliability scores for the target domains of politics and finance.

Target: politics			Target: finance
Source	κ (% agreement)	Interpretation^a	Source	κ (% agreement)	Interpretation^a
war	.854 (97.26%)	Almost perfect	war	.823 (98.08%)	Almost perfect
machine	.625 (94.51%)	Substantial	machine	.768 (94.23%)	Substantial
landscape	.711 (92.07%)	Substantial	landscape	.654 (87.50%)	Substantial
building	.734 (96.04%)	Substantial	building	.757 (96.15%)	Substantial
economy	.797 (97.56%)	Substantial	economy	N/A^b	N/A
living being	.678 (86.28%)	Substantial	living being	.711 (86.54%)	Substantial
weather	.576 (96.65%)	Moderate	weather	.852 (99.04%)	Almost perfect
game/sports	.635 (93.90%)	Substantial	game/sports	.519 (92.31%)	Moderate
journey	.528 (92.38%)	Moderate	journey	.558 (93.27%)	Moderate

^aInterpretation based on Landis and Koch (1977, p. 165); ^b Economics cannot be a metaphorical source domain when the target domain is related to finance.

Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159. doi:10.2307/2529310

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Data availability statement

The data and data-analytical procedures of the reliability tests and the small-scale analysis reported in this paper are publicly accessible on the Open Science Framework (OSF) at https://osf.io/9vxrs/.