The Role of Agency in Arendt's Theory of Judgment: A Principled Approach to Diversity on the Bench
Bibliographic record
Abstract
This article reflects on Hannah Arendt's work on political judgment in the context of considering judicial diversity. In particular, the author builds off Jennifer Nedelsky's work that, draws from Arendt's work on judgment to argue that a diverse judiciary is necessary for both truly autonomous and optimal judgment. Using Nedelsky's work as a foundation, the article considers one major outstanding question: if diversity is indeed justifiable and essential, is there any principled ground to assist us making choices over which standpoints to include?In answering this question, the author begins from the supposition that claims for increased judicial diversity contain background assumptions that particular kinds of difference have a special claim to inclusion. She argues that this supposition can be grounded within Arendt's theory of judgment through attention to her understanding of politics. In looking at her theory through such a lens, the author claims that the processes Arendt advocates are motivated by a desire to provide the conditions for the creation of shared narratives. Once examined further, however, it also appearsthat there is a necessary ethical premise underlying her vision - an equality of agency among community members. The author suggests that a focus on this premise can allow us to accord special status to the claims for inclusion of particular groups who experience a systemic denial of agency.
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.075 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.010 |
| 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".