Bibliographic record
Abstract
In the present editorial, we briefly describe some aspects of the domain of the scientific study of literature, the methods that have been used, and the nature of the theories that have been developed. We discuss some of the prior work that has been done on cognitive processing of and affective reactions to literary texts and how this interacts with the nature of the reader. We note that there is a need for further work on how the literary reactions vary with the reading context. We also describe some of the methods that have commonly been used, such as reading time, questionnaire responses, and protocol analysis. The potential for applying methods from cognitive neuroscience, such as the measurement of event-related potentials and brain imaging, is an exciting opportunity in the future. Finally, we identify some of the types of explanations that have been developed in the scientific study of literature, including variable relations and processing accounts. Other kinds of theoretical approaches, such as those based on complexity theory, might be needed in the future. Our conclusion is that although a great amount of further work needs to be done in understanding literature, there are a wide range of exciting possibilities.
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 | 0.022 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.011 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".