Bibliographic record
Abstract
In Cooper v Stockett, a plaintiff unsuccessfully claimed that a central character in the 2009 novel The Help was based on her and that the depiction caused her emotional harm. By analyzing the documents filed by the parties, this article argues that the plaintiff is best understood primarily as a reader. From this perspective, the relationship between plaintiff and defendant parallels that between reader and author on several levels. The plaintiff-reader uses both textual and extratextual information to judge the author’s moral fibre, especially her level of commitment to anti-racism, and attempts to engage the law to address what are essentially moral wrongs linked to race and representation. Textually, how the White author deploys literary strategies to convey moral messages within the novel generates a sense of moral dissonance in the Black plaintiff-reader, and extratextual factors, such as interviews with the author and legal arguments advanced by the defence team, work to exacerbate that sense of dissonance, undergirding the plaintiff’s conviction that she has been wronged. While the substantive law of personality rights and invasion of privacy are not particularly sympathetic to her project, the procedural process of the lawsuit nonetheless provides a forum for it.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.044 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".