Considerations when using the MIP and MIPVU

Another implication for metaphor researchers relates to the use of the MIP and MIPVU for identifying metaphor. As a method for identifying metaphorically related words, the MIP and MIPVU are fairly reliable. In fact, in this study, two raters using a modified version of the

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MIP/MIPVU on both spoken and written data agreed on whether a lexical unit was

metaphorically related over 96% of the time. Moreover, the VU Amsterdam Metaphor Corpus acts as a valuable resource for checking borderline cases, making the process even more

dependable. Yet, there was still disagreement among raters in certain cases. For example, in this study, the two raters coded “feeling” differently, with one rater considering “emotion” to be its basic sense so coding it as non-metaphorical and the other coder considering a feeling to mean “something that you feel physically in your body,” and thus coding it as a metaphor related word. As with the previous discussion of “before” and “after,” it seems like a stretch to consider the basic meaning of “feeling” to refer to physical sensations rather than emotions; however, that would be the results of strictly applying the MIP/MIPVU procedure to the language data in this study. Other disagreements between the two raters revolved around specific categories; that is, while both raters considered a word to be metaphorically related, they disagreed about how to categorize the word. For example, one coder categorized “transmit” your feelings as a metaphor related word while the other coder categorized it as language error. These categories were added to the original MIP/MIPVU procedure in order to further analyze the specific types of metaphor taught in the metaphor classes, so this last example is not a criticism of the MIP/MIPVU itself. However, it still shows that metaphor identification poses challenges for researchers more generally. Overall, while the MIP and MIPVU appear fairly reliable and straightforward on the surface, there are certainly some challenges when applying them to real language data.

Indeed, MacArthur (2015) questions the assumption that using a dictionary,in particular the MEDAL, will enable researchers to confidently determine the basic, contemporary sense of a word for analysis as stipulated by the MIP/MIPVU, an issue that also arose in this study and that has already been discussed above in relation to the lexical units “before,” “after,” and “feeling.”

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She describes the frustrations she experienced using the MEDAL to analyze a conversation in which a lecturer and a student were discussing the student’s written assignment about Oscar Wilde during office hours. As the lecturer used words like “say,” “contradict,” “tell,” “argue,” “talk,” and “speak,” Macarthur felt that clearly the lecturer was metaphorically framing the act of writing as an act of speaking. However, consultation with the MEDAL suggested that the first two of these verbs were not metaphorical, the next two were possibly metaphorical, and only the last two were definitely metaphorical, resulting in a somewhat incoherent and intuitively

incorrect analysis of the dialogue. She concludes by criticizing the overall MIP/MIPVU

procedure as the intense focus on word analysis can cause researchers to lose sight of the overall discourse, as well as the use of the MEDAL, which is a dictionary designed for language

learners, not metaphor researchers. Likewise, Dorst and Kaal (2012) describe the case of looking up “pandora’s box” and “artful dodger” in the MEDAL. The dictionary gives separate entries for each of these words, with an abstracted meaning and no explanation of the original literary source, meaning that these two expressions would be coded as non-metaphorical using the MIP/MIPVU. While acknowledging that most metaphor analysts would like to consider the original literary origins for these expressions as the most basic sense of the word and thus code them as metaphorical, Dorst and Kaal (2012) argue that on a linguistic level, these words should be coded as non-metaphorical to maintain the reliability of the MIP/MIPVU (pp. 65-66). These two researchers, then, appear to have opposite approaches to consulting the MEDAL for coding using the MIP/MIPVU, with MacArthur (2015) recommending a broader view of metaphor analysis with the MEDAL as a tool for extra consultation and Dorst and Kaal (2012) recommending strict adherence to the definitions within this particular source despite a researcher’s contradictory knowledge about the metaphoricity of a particular lexical unit.

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Clearly, even when using the MIP/MIPVU, there can be disagreements about the metaphoricity of specific lexical units and different approaches with regard to coding metaphor.

Furthermore, while the MIPVU groups metaphorically related words into direct, indirect, and implicit metaphor, researchers studying other types of metaphor may want to categorize metaphorically related words differently. Although coding for metaphor with the MIPVU is only the first step in metaphor analysis, researchers use this method to report on and discuss metaphor frequency; moreover, it is difficult to analyze something you have not identified. Researchers should develop reliable methods for testing other types of figurative language instead of only relying on the categories in the MIPVU. For example, Biernacka (2013) used the MIPVU and MIV as models to develop a procedure for identifying metonymy, enabling her to focus on metonymy instead of metaphor. As discussed in the methodology section, this study included language lessons focusing specifically on metonymy, simile, and personification in addition to metaphor; for this reason, metonymy, simile, personification, and language error were added to the category of metaphorically related words, facilitating both quantitative and qualitative

analysis. For example, it was possible to analyze metaphor error to notice how students struggled with conventionalized figurative expressions and collocations. It was also possible to analyze students’ use of similes on spoken and written narratives or their use of personification on the summary/responses and graph description task. Researchers who use the MIPVU for coding run the risk of focusing only on metaphorically related words and ignoring other interesting and productive categories of figurative language use.

Thus, it is recommended that researchers not only focus on more detailed analysis of other categories in addition to the general category of metaphorically related words, but also that procedures for identifying other types of metaphor continue to be refined to increase inter-rater

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reliability. While the efforts of Biernacka (2013) in developing a procedure for coding metonymy are laudable, more methods and tools are needed for other types of figurative language as well. Further analysis of different types of metaphor may make it easier for researchers to move beyond overall metaphor frequency into deeper analysis of how and why different types of metaphorical expressions are used. With more refined coding methods, researchers could more easily carry out qualitative analysis of the function of metaphor on a larger scale, which could help teachers and researchers analyze whether students are learning to use metaphor more effectively in their second language. At the same time, however, the process of categorizing metaphorically related words still needs to be refined so that inter-rater reliability on distinct categories can be improved.

To sum up, the MIP and MIPVU generally lead to high inter-rater reliability and is fairly straightforward; however, researchers may still disagree about the metaphoricity of specific lexical units. Since discrepancies can arise even after consulting the MEDAL, these differences should be further discussed and resolved on a case by case basis among researchers, and it should be accepted that some degree of subjectivity will be inherent in any coding of real language data due to the nature of metaphor itself. In addition, since the MIP/MIPVU only identifies metaphor related words, it was suggested that researchers consider coding for other types of figurative language as well. However, this process still needs to be further refined to improve inter-rater reliability across multiple categories of figurative language use.