Theorizing the Role of Metaphors in Co-orienting Collective Action Toward Grand Challenges: The Example of the COVID-19 Pandemic
Bibliographic record
Abstract
Abstract This paper adds to the literature on societal grand challenges by shifting the focus away from business firms and other formal organizations as key actors in addressing such challenges toward the inherent organizing capacity that lies in the use of language itself. More specifically, we focus on the organizing capacities of metaphor-based communication, seeking to ascertain which qualities of metaphors enable them to co-orient collective action toward tackling grand challenges. In addressing this question, we develop an analytical framework based on two qualities of metaphorical communication that can provide such co-orientation: a metaphor’s (a) vividness and (b) responsible actionability. We illustrate the usefulness of this framework by assessing selected metaphors used in the public discourse to make sense of and organize collective responses to the Covid-19 pandemic, including the flu metaphor/analogy, the war metaphor, and the combined metaphor of “the hammer and the dance.” Our paper contributes to extant research by providing a means to assess the co-orienting potential of metaphors in bridging varied interpretations. In so doing, our framework can pave the way toward more responsible use of metaphorical communication in tackling society’s grand challenges.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".