A troubled inheritance: Overcoming the temporality problem in cases of historical injustice
Bibliographic record
Abstract
Debates about historical injustices face the “temporality problem”; they need to explain how the living can, on the one hand, be held accountable, and, on the other hand, be made whole relative to wrongs committed in the distant past. In this paper, we critically examine and object to three influential views on offer: enduring or structural accounts, institutional liability accounts, and national community accounts. We argue that while these views are initially attractive, each in their own way, they face a common problem: they appeal to contingent features of historical injustices. Consequently, they struggle to capture real or very similar cases. We propose a novel account in which inherited agency, which emerges from lineages of social learning, links past and present generations. Given that social learning is a non-contingent feature of intergenerational communities, it provides a stronger solution to the “temporality problem”. To be a member of an intergenerational community is to be the product of one or more lineages of social learning and to inherit a ledger of moral claims and debts from past generations of those very lineages.
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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 | 0.030 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.034 |
| Scholarly communication | 0.011 | 0.039 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 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".