Reimagining Government with the Ethics of Care: A Department of Care
Bibliographic record
Abstract
The question of how to apply care ethics to institutions and social policies has been much discussed, with recent research expanding the scope of care ethics policy analysis to policy areas that are generally not viewed as ‘care related’. This paper seeks to engage with this literature in a critical and constructive way to explore more fully the transformative potential of the ethics of care. In particular, this paper argues that the aforementioned literature uses care ethics to focus on practices of care, as opposed to employing the ethics of care as a critical political theory (Robinson, Fiona. 2018. “Resisting Hierarchies through Relationality in the Ethics of Care.” International Journal of Care and Caring XX (xx): 1–13). While such analyses are important, this paper proposes a ‘Department of Care’ as a thought experiment to demonstrate how the ethics of care, as a critical political theory, allows for a radical critique of institutions and governing norms, and inherently destabilizes the dominant understandings of the purpose, structure, and role of government and public policy.
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 | 0.052 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.106 |
| Scholarly communication | 0.030 | 0.025 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.014 | 0.022 |
| Insufficient payload (model declined to judge) | 0.004 | 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".