Distributive equality, relational equality and preferences about higher education
Bibliographic record
Abstract
Are scenarios in which disadvantaged students prefer not to attend (certain) universities a concern from the perspective of an egalitarian theory of justice? I consider this question from the respective perspectives of two prominent approaches to equality: distributive theories, which focus on the fairness of inequalities in outcomes, and relational theories, in which equality is conceived in terms of the relations among individuals. While distributive theorists seek to directly assess the fairness of distributions, relational theorists argue that our assessment of distributions must be guided by broader concerns of relational equality. As I suggest in this article, this difference in approach is also reflected in the debate about justice and education, in particular in the context of the debate about whether justice in education requires some form of equality or, rather, as theorists such as Elizabeth Anderson and Debra Satz have suggested, ‘adequacy’. I suggest that both distributive and relational theorists can identify as problematic that disadvantaged students prefer not to go to university (or certain universities), though they will have different interpretations of what precisely is problematic about such a scenario and how it relates to inequality of the relevant kind. However, this judgment is susceptible to the objection that it is counterintuitive that preferences that individuals endorse and identify with would be the source of unfair inequalities. In response, I argue that both distributive and relational egalitarians have good reason to challenge this objection; individuals’ preferences, even if the individuals themselves endorse these preferences, should not fall beyond the purview of egalitarian theory. The final section returns to the theoretical debate between distributive and relational approaches to equality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".