Bibliographic record
Abstract
Conversations on EmpathyIn the aftermath of a global pandemic, amidst new and ongoing wars, genocide, inequality, and staggering ecological collapse, some in the public and political arenas have argued that we are in desperate need of greater empathy -be this with our neighbours, refugees, war victims, the vulnerable, or disappearing animal and plant species.This interdisciplinary volume asks the crucial questions: How does a better understanding of empathy contribute, if at all, to our understanding of others?How is it implicated in the ways we perceive, understand and constitute others as subjects?Conversations on Empathy examines how empathy might be enacted and experienced -either as a way to highlight forms of otherness or, instead, to overcome what might otherwise appear to be irreducible differences.It explores the ways in which empathy enables us to understand, imagine, and create sameness and otherness in our everyday intersubjective encounters focusing on a varied range of "radical others" -others who are perceived as being dramatically different from oneself.With a focus on the importance of empathy to understand difference, the book contends that the role of empathy is critical -now more than ever -for thinking about local and global challenges of interconnectedness, care and justice.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.011 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.028 | 0.008 |
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".