Justice, Gender, and Corporations. Outline of a Feminist Political Philosophy of the Corporation
Bibliographic record
Abstract
Abstract Corporations, as institutions that participate in the creation and perpetuation of gender-based injustices, have been neglected by feminist political philosophers and egalitarians in general. However, since gender-based inequalities within the family, the market, and in democratic participation are interconnected, critically scrutinizing institutions such as corporate organizations appears to be essential in order to achieve gender justice. This is our goal in this paper. In the first part, we look at the (surprising) domination of the ethics of care in the feminist literature on corporations. Since it focuses essentially on the goal of developing virtuous managers, we conclude that this ethics of care is misleading when it comes to thinking about the kinds of relations that characterize corporations and their much needed organizational transformation. In the second part, we attempt to highlight and articulate more explicitly the need for a critical analysis of corporations from the point of view of gender justice. Finally, having shown how purely “distributive” approaches of gender justice are unsatisfactory, we finish by outlining a multidimensional approach to gender (in)justice within and by corporate organizations. We do so by drawing on the insights of distributive, participatory, and relational accounts of equality.
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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 | 0.011 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.060 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| 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".