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Record W4412708738 · doi:10.1080/02773945.2025.2533751

Empathy as Bug: The Rhetoric of MAGA’s “Battle”

2025· article· en· W4412708738 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueRhetoric Society Quarterly · 2025
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsCarleton University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsRhetoricBattleEmpathyLiteraturePsychologyPhilosophyHistoryArtSocial psychologyLinguisticsAncient history

Abstract

fetched live from OpenAlex

This essay critiques the rhetorical displacement of empathy by sympathy in contemporary political discourse, especially within digital media ecologies dominated by memes, grievance, and identitarian performativity. Beginning with Elon Musk’s claim that empathy is “civilizational suicide,” the essay traces how sympathetic identification—rooted in sameness and affective fusion—has supplanted empathy’s difficult labor of encountering difference. Drawing on rhetorical theory, affect studies, and close readings of memes, the essay analyzes how contemporary rhetorics (including on the Left) impede the slow, uncertain, and unsentimental work that empathy requires. Turning to Hannah Arendt’s Eichmann in Jerusalem and theories of rhetorical empathy, the essay reframes empathy not as moral sentiment but as agonistic hearkening—a practice of nonidentical attunement amid algorithmic closure. Ultimately, it calls for rhetorical scholars to reclaim empathy as a counter-rhetorical and ontological necessity in the face of post-truth tribalism.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.143
Threshold uncertainty score0.498

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.302
Teacher spread0.290 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it