Metaphor as a lens through which to examine deep, personal, emotional experiences
Bibliographic record
Abstract
Language is the most sophisticated tool humans have developed to communicate emotional experiences. However, due to their highly personal, idiosyncratic nature, such experiences can often be difficult to put into words. The dimensional aspects of emotional experiences, i.e., those involving valence, dominance and arousal can be expressed through literal language. However, once we go beyond these dimensional accounts and try to describe the subjective felt experiences of emotions it is necessary to employ metaphor. Metaphors provide information about the rich detail of human emotional experience in a way that literal language does not (Semino et al., 2017). Metaphorical skill and imagination are therefore important in communicating the nature of unshared experience and creating rapport (Reisfield & Wilson, 2004). For this reason, when talking about deep, personal emotional experiences that are difficult to describe, people often reach for metaphor, with more personal experiences leading to more creative uses of metaphor (Fainsilber & Ortony, 1987; Williams-Whitney et al., 1992). Possible reasons for this are the fact that metaphors allow us to describe ineffable experiences in more concrete, tangible, and often physical terms, and metaphors are sufficiently flexible to allow us to employ a finite set of linguistic resources to express precisely, an infinite range of experiences (Colston & Gibbs, 2021). In this talk I present findings from four studies, which involved the analysis of metaphor in order to better understand people’s personal, emotional experiences. These experiences include: pregnancy loss; bereavement; the description of positive and negative workplace experiences; and older adults’ experiences of lockdown during Covid 19. Through these studies I reveal what metaphor analysis can tell us about: embodied grief; agency and identity; relationships with others, including the deceased; attitudes towards time, the extent to which experiences are shared by communities; and the ways in which these experiences change over time. I reflect on the benefits and drawbacks of the methods employed and discuss the ways in which findings from some of these studies have been incorporated into professional development materials that have been designed for those who support the bereaved. References Colston, H. L., & Gibbs, R. W. (2021). Figurative language communicates directly because it precisely demonstrates what we mean. Canadian Journal of Experimental Psychology/Revue Canadienne de Psychologie Expérimentale. Fainsilber, L., & Ortony, A. (1987). Metaphorical uses of language in the expression of emotions. Metaphor and Symbol, 2(4), 239–250. Reisfield, G. M., & Wilson, G. R. (2004). Use of metaphor in the discourse on cancer. Journal of Clinical Oncology, 22(19), 4024–4027. Semino, E., Demjén, Z., Hardie, A., Payne, S., & Rayson, P. (2017). Metaphor, cancer and the end of life: A corpus-based study. Routledge. Williams-Whitney, D., Mio, J. S., & Whitney, P. (1992). Metaphor production in creative writing. Journal of Psycholinguistic Research, 21(6).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.029 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".