Sensemaking Through Metaphors: The Role of Imaginative Metaphor Elicitation in Constructing New Understandings
Bibliographic record
Abstract
Drawing on in-depth interviews with exchange and international students during the COVID-19 pandemic, we elaborate on the role of Imaginative Metaphor Elicitation (IME) to generate knowledge about participants’ experiences while helping them make sense of and cope with a difficult situation. Imaginative metaphors allow participants to explore feelings, assumptions, and behaviors in non-threatening ways and facilitate introspection and self-awareness. We propose that imaginative metaphors help participants make their experience tangible and accessible, identify problematic assumptions, behaviors, as well as resources available to them. Some reported gaining a renewed sense of empowerment. Simultaneously, IME provides an opportunity to collect rich data while co-creating solutions for and with participants. We contribute to calls for embedding social impact in the research design by highlighting the value of IME in gaining deeper access to participants’ experiences while supporting them in taking an active role in their situations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".