A reader response method not just for ‘you’
Bibliographic record
Abstract
This article contributes to empirical literary studies by offering a new reader response method for examining targeted textual features. With the aim of further establishing the new paradigm of reader response research in stylistics, we utilise a Likert scale – a tool that is usually used to generate data that is analysed quantitatively – to elicit qualitative data and, crucially, show how that data can be synthesised with an analysis of the primary text to provide empirically based conclusions relevant to particular textual features for cognitive narratology and stylistics. While we offer a new method that can be used to investigate textual features in all kinds of text, we exemplify our approach via the investigation of second-person narration in geniwate and Larsen’s digital fiction The Princess Murderer and provide a new understanding of the experiential nature of ambiguous forms of ‘you’ in fiction. Our stylistic analyses show how responses can be generated by linguistic features in the text. We then analyse reader responses to those examples and show that this can provide a more nuanced account of ‘you’ narratives than a stylistic analysis alone because it affords insight into how different readers do or do not psychologically project into and/or assume the role of ‘you’. Our results represent the first time that current typologies of the second person have been empirically tested and we are the first study to find an empirical basis for doubly deictic ‘you’. We therefore contribute a new empirically based understanding of how readers experience ambiguous forms of ‘you’ in fiction.
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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 | 0.069 | 0.179 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.011 |
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".