My Sister, My Enemy: Using Intersectional Readings of Hagar, Sarah, Leah, and Rachel to Heal Distorted Relationships in Contemporary Reproductive Justice Activism
Bibliographic record
Abstract
Using a feminist hermeneutic, Autumn Reinhardt-Simpson attempts to set out in this article how a third- or fourth-wave intersectional reading of the stories of Hagar and Sarah and Leah, Rachel, and their maids can become a source of both truth and healing within feminist activist communities today, particularly those working for reproductive justice. Reinhardt-Simpson identifies several issues within the stories such as societal acceptance of women who seek power only within patriarchal constructs or to benefit the aims of patriarchy, as well as issues that divide women both then and today such as class, race, and status and the way that women relate to each other within a patriarchal structure. Reinhardt-Simpson concludes that a liberatory reading of these stories can point us toward reconciliation with our sisters.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.048 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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".