Book Note: Why Love Leads To Justice: Love Across The Boundaries, by David A.J. Richards
Bibliographic record
Abstract
MUCH FEMINIST SCHOLARSHIP HAS GRAPPLED with the concept of love in its many forms. Modern love has been understood as a manifestation of harmful patriarchal values, a “curse”2 that confines women to rigid gender norms of femininity and passivity.3 However, love has been reclaimed and reconceptualised by some feminists as a powerful force for resisting these patriarchal norms and encouraging self-realization among women, men, and others.4 David A.J. Richards’s Why Love Leads to Justice makes a valiant effort on the latter understanding.5 His thesis is simple: love leads to justice.6 In particular, love that is transgressive, which crosses the boundaries of the existing “Love Laws,” leads to justice.7 He uses Love Laws to refer broadly to law aimed at criminalizing and otherwise prohibiting sexual and loving relations between certain classes of people. The book narrows in on two kinds of transgressive love: adultery and gay and lesbian love. Using the intimate stories of prominent artists and social activists of the nineteenth and twentieth centuries, Richards draws on the linkages between their personal and public lives to demonstrate a reciprocal empowerment between the two domains. Life in love across legal boundaries is shown to be an act of resistance to patriarchal injustice. At the same time, the stories demonstrate how transgressive love has allowed for the healing of moral injury done to the protagonists by Love Laws designed to suppress and marginalize them. Groundwork is laid for these ideas in the first chapter by looking at the adulterous relationships between George Henry Lewes and Marian Evansknown widely by her penname, George Eliotas well as Harriet Taylor and John Stuart Mill.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".