Bibliographic record
Abstract
The study of realist fiction can illuminate and expand dialogical theories of the self, especially those originating in M.M. Bakhtin’s writings. To overcome the criticism that the study of the literary criticism surrounding major works of fiction has no place in psychology and needs no input from it, I advance the concept of the Edited Other. The concept states that, in analytic and related forms of psychotherapy, specific and paramount modes of self-presentation are represented within the context of theoretical constructs, while, in works of realist fiction, authors isolate characters’ dominant characteristics within the context of novels’ plots. To instantiate the Edited Other I choose one of Mario Vargas Llosa’s novels ( The War of the End of the World). I select Vargas Llosa because of his mastery of varied forms of novel, his well-developed moral philosophy, and his deep understanding of literature’s role in society. Furthermore, The War of the End of the World, almost uniquely, can be compared to a supposedly factual account of the events portrayed in the novel (Euclides da Cunha’s Rebellion in the Backlands). I use the Edited Other in order to demonstrate that Vargas Llosa, by using fictional characters, provides contemporary readers with bridges linking them to and giving them imaginative access to historical events.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.034 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".