Bibliographic record
Abstract
Some researchers feel so close to certain literary works that they may have trouble looking at them critically. The work I hold in this special esteem is Les Liaisons dangereuses (1782), by Choderlos de Laclos, which taught me, as a teenage girl, how the world works—or so I thought. What does this blind spot in my research imply about the way I believe we treat literature as professional critics? Specifically, how are critical approaches to literature unable to account for the complex relationship between our lives and our readings? This essay concerns my experience reading Laclos’s text and how I felt the need to protect it from literary analysis. I also consider new methods for studying literature, such as those proposed recently by Rita Felski, which include our subjective reactions to our readings. Despite these new methods, Les Liaisons remains rebelliously outside the scope of my academic research.
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 | 0.030 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.025 | 0.078 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".