Bibliographic record
Abstract
I am the man who comes and goes between the bar and the telephone booth. Or, rather: that man is called “I” and you know nothing else about him ( If on a Winter’s Night a Traveller , Italo Calvino) The preceding chapter has outlined the concept of a narrative space as a textually prompted construct used in story construction. Narrative spaces can thus be distinguished through their temporal dimension, a consistent subplot, or the construction of a specific epistemic point of view. I have also explained how narratives achieve coherence through levels of blending leading to an emergent story. However, reading a work of fiction invariably assumes a text-mediated contact with the constructed fictional subjectivity often referred to as ‘the teller’ or ‘the narrator.’ The concept is textually constructed, which has been stressed repeatedly, primarily in order to avoid simplifications whereby the narrator is identified with the author. At the same time, the illusion is compelling in many narratives, and the reason why the author is so naturally understood to be responsible for the way the story is told has not been answered in sufficient depth. While the ‘death of the author’ (as a viable narrative concept) has been widely publicized, the news may be exaggerated, as some flesh-and-blood authors begin to claim their right to be considered legitimate participants in the narrative exchange.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.008 |
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".