Bibliographic record
Abstract
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.
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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.028 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| 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".