Grave Injustice: Disrespect Toward the Dead, Transgenerational Publicity, and Reparations
Bibliographic record
Abstract
This article argues the legitimacy of a political society depends on adopting a framework of justice that grants status to, and avoids displaying disrespect toward, the dead victims of grave historical injustices connected to that society.Taking insights from Johann Fichte and Immanuel Kant's accounts of justice and the dead, I offer five parameters for avoiding disrespect toward the dead.If the framework of justice officially adopted today had operated in historical contexts, it should have (1) counted as wrongs what was done to dead victims of injustice (those alive then but dead today) and (2) avoided incentivizing dead perpetrators (those alive then but dead today) to commit injustices in their own time.We should (3) make judgments about cases where dead victims suffered injustice and form "settled convictions" for which our framework of justice should account.We should (4) articulate why intergenerational duties of reparation would or would not survive the death of historical victims.We imagine whether the dead victims would have felt disrespected had they known our current framework (a transgenerational publicity principle).( 5)A framework should consider wrongs from context-shifts.This approach provides reasons for reparations and redressing injustices, especially for Indigenous peoples.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.053 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".