Voices speaking to and about one another: introducing the Project Dialogism Novel Corpus
Bibliographic record
Abstract
We introduce a new dataset for the computational analysis of novels: the Project Dialogism Novel Corpus (PDNC). The PDNC currently consists of 22 novels in which all quotations are identified and annotated for speaker, addressee(s), and characters mentioned. PDNC is by an order of magnitude the largest corpus of its kind. Each novel is annotated manually by a pair of annotators using customized software we developed. In addition to releasing the dataset itself alongside this paper, we are also releasing the custom annotation software we developed (including the source code) along with our annotation guidelines. In the discussion section, we present two applications of the PDNC from our own research: quote attribution and emotion dynamics. We argue that the PDNC will promote a more nuanced and accurate view of novelistic discourse; whereas much research currently envisions the novel as expressing the voice of the author, the PDNC presents novels as a polyphonic fabric of characters’ voices.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".