Bibliographic record
Abstract
What would a phenomenology of justice look like and what role would mercy play in that account? The unruly experiences and lives of the individuals and communities wrapped up in the dramas of justice are paradoxically distant from legal and philosophical reasoning, laundered by rules of evidence for the instrumental exigencies of the former, and frequently effaced by the disciplinary conventions of the latter. One casualty of these habits of reflection is our understanding of the role of mercy in the experience of justice. Wanting to recapture space to imagine the role of mercy in justice, this paper makes an exploratory turn to a world consumed with representing the messy experience of justice and still thick with the language of mercy – to the poetic and narrative world created in the Book of Jonah. Drawing inspiration from a close reading of this mythic tale, I argue that mercy is an essential feature of the phenomenological architecture of justice, requiring us, as it does, to connect abstract judgment with the complexities and exigencies of our concrete conditions. Though distant from contemporary legal and political theory, I argue that mercy in fact remains an uncanny aspect of our experience of justice and so demands a political and legal scholarship that spends as much time reflecting on the sources and nature of mercy as a political virtue, as it does on the demands of reason and the dictates of law alone.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.071 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".