Necessarily a narrator or narrator if necessary: A short note on a long subject words
Bibliographic record
Abstract
Abstract For (at least) literary narratives I propose to understand “narrator” as follows: An inner-textual speech position from which the current narrative discourse as a whole originates, and from which references to the entities, actions and events that this discourse is about are being made. Through a dual process of metonymic transfer and anthropomorphisation the term “narrator” is then employed to designate a presumed occupant of this position, the hypothesized producer of the current discourse. A narrator is a linguistically indicated, textually projected and readerly identified position whose occupant needs to be thought of primarily in terms of a communicative role, distinct from any actualworld flesh-and-blood (or computer) producer of the text. The paper describes in brief eight different kinds of general considerations (linguistic, philosophical, methodological and general literary-theoretical) which can motivate a narratologist to judge the narrator category/instance as an indispensable or as a merely optional element of his general model of literary narrative. The article concludes with two recent theoretical moves which tend to circumvent the need for such a choice by either re-drawing the narratologist's domain of objects or by redefining the status of the narrator category itself.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".