At Play on the Borders of the Diegetic: Story Boundaries and Narrative Interpretation
Bibliographic record
Abstract
Working with young readers, aged 10 to 14, as they responded to narrative texts in a variety of media (Mackey, 2002), I observed a recurring phenomenon: In a variety of ways they repeatedly stepped in and out of the fictional universe of their different stories. Some examples will perhaps give the flavor of this experience: Two 14-year-old girls playing Starship Titanic alternate between lively engagement in the narrative world of the story and stepping outside the fiction to console themselves, “Oh well, if we die, we can just start again.” A 10-year-old girl speaks of alternating between the novel and the computer game of My Teacher is an Alien, using the novel as a source of game-playing repertoire. Two 10-year-old boys look at the DVD of the film Contact, learning how the special effects of an explosion scene were composed, and commenting on how their new awareness of scene construction would affect how they view the film in the future. As I recorded and analyzed numerous examples of such behaviors, I was struck by a common element of interpretive activity on the boundaries of the fictional universe. Sensitized to the topic, I began to notice, and then to collect, examples of contemporary texts that foster various forms of such border crossing, in and out of the diegesis, the framework of events as narrated in the text. This article explores how an awareness of this aspect of contemporary texts may enhance our understanding of interpretive processes and expand what happens in literature classes.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.028 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".