Reading anger, compassion and longing in Beatrice Culleton Mosionier’s In Search of April Raintree
Bibliographic record
Abstract
Abstract This article engages with polarizing debates about compassion by exploring the relationship between this emotional response and anger in Beatrice Culleton Mosionier’s In Search of April Raintree. While Martha Nussbaum argues that compassion functions as an ethical bridge linking one person to the next, affect theorists argue that compassion reaffirms unequal relations of power. This article maps the ways Mosionier’s novel might evoke the reader’s compassion, and investigates the role of this response by focusing on a narrative pattern where April experiences abuse, expresses intense anger at her suffering and then longs for markers of privilege such as white skin and affluence. This article contends that April’s anger interrupts the potential for passive compassion, and foregrounds the social stratification that gives rise to April’s suffering.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".