Are Metaphors Ethically Bad Epistemic Practice? Epistemic Injustice at the Intersections
Bibliographic record
Abstract
Abstract The COVID-19 pandemic brought the debate about the ethics of metaphors to the fore. In this article, I draw on blending theory—a theory of cognition—and theories of epistemic injustice to explore both the epistemic and ethical implications of metaphors. Beginning with a discussion of the conceptual alterations that may result from the use of metaphors, I argue that the effects these alterations have on available hermeneutical resources have the potential to result in a type of hermeneutical injustice distinct from the “lacuna” described by Miranda Fricker (Fricker 2007). Following, I examine how metaphors may therefore be considered “ethically bad epistemic practice,” as described by Rebecca Mason, because of how they may contribute to perpetuating an inequitable epistemic status quo (Mason 2011). Yet these same features may be used to promote epistemic justice in the context of intersectional power relationships. Situating the effects of metaphors within an inequitable yet dynamic epistemic system, I argue that foregrounding intersectional power dynamics enables us to interrogate the ethics of metaphors with consideration of both the epistemic and material consequences that may occur. I conclude by providing guidance for how, given that metaphors do epistemic work, we may use them to do ethical epistemic work.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.091 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".