The Poor Man’s Ewe Lamb (2 Sam 12:1–4) in Intersectional, Interspecies Perspective
Bibliographic record
Abstract
Abstract Nathan tells David a story about a rich man who takes and kills a poor man’s lamb (2 Sam 12:1–4). This, it turns out, is figurative for David’s own deeds of killing Uriah the Hittite and taking his wife. The story and its application suggest the intersecting power dynamics between groups: rich and poor, male and female, native and foreigner—and, crucially, human and nonhuman. This article argues that intersectional analysis should include an interspecies dimension, and explores these dynamics at work through various mechanisms of relation. Low status human groups are connected with nonhumans through animalisation, and are thereby delegitimised. Nonhuman animals and animalised humans are positioned as objects within mechanisms of domination, such as exploitation, exchange, and semiosis. The relationship between the poor man and lamb, though, offers another possibility: alliance. Care can be extended across species lines, with implications for intergroup relations throughout the intersectional web.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.016 | 0.022 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".