Mobilizing Empathy: From Einfuhlung to Homo Empathicus
Bibliographic record
Abstract
This dissertation traces the movements of empathy across and within diverse contexts. Empathy is shown to be conceptually amorphous with significant degrees of variation in its applications. With an analytic lens focused on use (conceived of as the mobilization of empathy) heterogeneous conceptions of empathy are examined, illuminating the different psychological and social realities that are created when empathy functions in different ways. This systematic reconstruction is facilitated through an analysis of empathys moral, relational, epistemic, natural, and aesthetic conceptual foundations, and its quantitative, gendered, pathological, political, educational, commodified, and professional uses. It is argued that at the core of empathy is a moral valence; specifically, that empathy is irreducibly connected to ethical questions and, thus, there is always a moral dimension inherent in its applications. Based on the reconstruction an ontology of empathy is derived that includes the individual, the other, and its moral valence. The dissertation concludes with considerations of the consequences of this ontology. Challenging empathy exclusively construed as a matter of individual intentionality, it is argued that socio-political, economic, and societal structures create, shape, and maintain much of what individuals have access to and experience empathically. For this critical understanding, the notion of empathy avoidance, arm-chair empathy, and regulated empathy, are introduced.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".