Bibliographic record
Abstract
Abstract This paper proposes an approach to narrative deixis which offers a coherent analysis of the respective roles of proximal and distal deictic expressions (demonstratives as well as temporal and locative adverbs). The paper starts by arguing that fictional narratives require an approach to deixis which modifies a number of broadly held assumptions, especially as regards the interaction between tense and other deictic forms. It then considers the widely discussed instance of the temporal adverb now in the context of Past Tense. The second part of the paper gives special focus to demonstratives in narrative fiction, showing their role in temporal construals. It argues that both temporal and demonstrative expressions are primarily used to serve narrative viewpoint construction (which includes but is not limited to temporal viewpoint). Examples from several novels are then used to show how the proximal and distal choices of demonstratives, temporal adverbs and locative adverbs structure narrative viewpoint, including narrative representation of character experience. The paper concludes by proposing that in the context of fictional narratives the proximal/distal contrast is more relevant to meaning emergence than individual aspects of deixis, and that the construal of time can be achieved through the whole spectrum of deictic forms, not just tense and temporal adverbs such as now and then .
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".