Migrating Metaphors: Why We Should Be Concerned About a ‘War on Mental Illness’ in the Aftermath of COVID-19
Bibliographic record
Abstract
In the aftermath of the ongoing COVID-19 pandemic, there is a predicted (and emerging) increase in experiences of mental illness. This phenomenon has been described as “the next pandemic”, suggesting that the concepts used to understand and respond to the COVID-19 pandemic are being transferred to conceptualize mental illness. The COVID-19 pandemic was, and continues to be, framed in public media using military metaphors, which can potentially migrate to conceptualizations of mental illness along with pandemic rhetoric. Given that metaphors shape what is considered justifiable action, and how we understand justice, I argue we have a moral responsibility to interrogate who benefits and who is harmed by the language and underlying conceptualizations this rhetoric legitimates. By exploring how military metaphors have been used in the context of COVID-19, I argue that this rhetoric has been used to justify ongoing harm to marginalized groups while further entrenching established systems of power. Given this history, I present what it may look like were military metaphors used to conceptualize a “mental illness pandemic”, what actions this might legitimate and render inconceivable, and who is likely to benefit and be harmed by such rhetorically justified actions.
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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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.065 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.011 | 0.016 |
| 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".