Why It Matters: The Value of Literature as Object of Inquiry in Qualitative Research
Bibliographic record
Abstract
Literature is often excluded from the realm of social science, even by qualitatively minded scholars. While research drawing on non-fiction documents, such as a historian might undertake, is usually lent legitimacy and seriousness, many view research in literature as an artistic domain best left to literary scholars. Novels, however, provide unique insights into important social questions, and social scientists cannot afford to ignore them. Furthermore, the distinctions between novels and social scientific works are ambiguous, and these domains share a great deal in both form and aim. This sharing cuts both ways, with novelists offering social insights comparable to those of the anthropologist or historian and the social scientist borrowing literary devices for the elaboration of his or her ideas.
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 | 0.523 | 0.545 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.022 | 0.183 |
| Scholarly communication | 0.037 | 0.050 |
| Open science | 0.007 | 0.024 |
| Research integrity | 0.013 | 0.012 |
| 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".