Bibliographic record
Abstract
This essay takes as its focus the work of Mikhail Bakhtin, a leading literary theorist who has, to date, received relatively little attention in the field of law and literature. Central themes in the work of Bakhtin include the dialogic quality of the novel and its ethical implications, its commitment to singularity and context, its comprehensive critique of authority and, tying all of these features together, the importance of form and style to our understanding of the experience of literature. These themes all pay attention less to the subject-matter or content of particular works of literature than to its historical trajectory, its distinctive characteristics, and its approach to language. This is perhaps one important way in which Bakhtin’s approach is distinct from the focus on ‘‘narrative’’ which is so much the staple of law and literature writing. The novel’s ‘‘heteroglossia,’’ as Bakhtin puts it – its irony, ambiguity, and ‘‘doubleness’’ – is neither a serendipitous features of certain texts nor a secondary element of normatively driven ‘‘story-telling,’’ but structural and stylistic characteristics of the novel as such. Bakhtin thus problematizes the treatment of narrative and the authenticity of voice assumed in much of the standard literature. While Bakhtin brings to these arguments an unprecedented sweep and command of detail, and a highly distinctive vocabulary for analysis, his arguments parallel the work of the other twentieth-century writers on literature. In particular, writing at the same critical historical moment, Bakhtin’s claims are echoed in D.H. Lawrence’s own essays on the novel, and illustrated – indeed, performed – in his fiction.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| 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".