Aboutness and Meaning: How a Paradigm of Subject Analysis Can Illuminate Queer Theory in Literary Studies
Bibliographic record
Abstract
This paper uses the paradigms of subject analysis in information studies to study the treatment of homosexuality in academic literary criticism. Both subject analysis and contemporary gay and lesbian culture are concerned with the distinction between “aboutness,” defined as intrinsic intellectual content, and “meaning,” defined as the various uses to which a user might put that content. An examination of the treatment of homosexuality in various critical analyses of Melville’s Billy Budd suggests that literary critics are divided on whether homosexuality is part of the story’s content, or merely part of an interpretive strategy. Furthermore, trends in literary theory have questioned the possibility that we can find any innate “aboutness” in any literary work. Nonetheless, gay-positive readings of literature, particularly works of queer theorists like Eve Kosofsky Sedgwick, are re-enacting the activities of subject analysis in their works: placing literary works within broader contexts of literary, social and intellectual relationships. Furthermore, Sedgwick’s binarism between homosexuality as an explicit and visible cultural minority and homosexuality which pervades culture as a whole recreates the aboutness/meaning dichotomy of subject analysis. The paper concludes that literary theory and subject analysis, while very different, exist on a continuum with each other, and that each can benefit from the insights of the other.
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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 | 0.023 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.010 | 0.103 |
| Scholarly communication | 0.026 | 0.033 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".