Do we have relational reasons to care about intergenerational equality?
Bibliographic record
Abstract
Relational egalitarians sometimes argue that a degree of distributive equality is necessary for social equality to obtain among members of society. In this paper, we consider how such arguments fare when extended to the intergenerational case. In particular, we examine whether relational reasons for distributive equality apply between non-overlapping generations. We claim that they do not. We begin by arguing that the most common reasons relational egalitarians offer in favour of distributive equality between contemporaries do not give us reasons to object to distributive inequality between non-overlapping generations. This argument by itself however will not fully suffice to show that there are no relational reasons to care about intergenerational distributive equality, given the nature of relational equality and its requirements in the intergenerational case are likely to be qualitatively different than in the contemporary case. Therefore, we also make the positive argument that for the intergenerational case to satisfy the requirements demanded by the ideal of relational equality it suffices that future persons’ interests are meaningfully incorporated and protected in the decision-making of preceding generations, and there is no basis for a concern with distributive equality. While some have argued that the one-way and asymmetrical causal influence between non-overlapping generations means concerns of social equality are inapplicable in the intergenerational case, we argue that the ongoing nature of this influence makes concerns of social equality appropriate. If successful, the upshot of the argument is that it can be coherent to maintain a commitment to relational equality between non-overlapping generations, all while remaining agnostic about distributive equality between them.
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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.039 |
| Scholarly communication | 0.011 | 0.026 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".