Framing the Frame: Embedded Narratives, Enabling Texts, and Frankenstein
Bibliographic record
Abstract
Reader-focused analyses of frame narrative (usually assuming and elaborating a liminal, distinguishing, or transitional “picture-frame” metaphor) are incomplete, describing only the initial experience of “coming at” and “moving off from” the text as a pre-existing artifact. An alternative analysis would emphasize narrative acts and enabling texts (guided by the metaphor of an internal, form-giving “frame-work”), and thus describe the process by which the textual artifact comes into being, shaping itself over time into the text we eventually read. In Mary Shelley'sFrankenstein,we might distinguish three frame sequences: areadingsequence, anactionsequence, and anarrativesequence. The narrative sequence is the primary, enabling frame shaping the novel, and is dependent upon three levels of narrative refiguring:rhetorical,elemental, andintentional. Each narrative act in Shelley’s novel is enabled and shaped by a previous narrative act, and each narrative text produced by these acts is the peculiar result of the narrative sequence that engenders it. The tension between narrative act and narrative text inFrankensteinforms a fundamental dialectic process, producing an ambiguously authoritative artifact.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.029 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".