Bibliographic record
Abstract
In isolation texts, including literary texts, have no significance. They express thoughts and feelings and refer to social reality, but can do so only because every text has been written or pronounced in response to other texts. This is what theorists of intertextuality and rewriting, such as Julia Kristeva and Michael Riffaterre, maintained. In 1969 Kristeva wrote in a now classical phrasing: se construit comme mosaique de citations, tout est absorption et transformation d'un autre texte (146). Almost ten years later Riffaterre posited: text always refers to something said otherwise and elsewhere (138). Here intertextuality is raised to a general principle of writing, but, if intertextuality is everywhere, how can it be made amenable to research? Is it a concept that can be used in the analysis of texts and contribute to our understanding of verbal communication?
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 | 0.039 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.011 | 0.082 |
| Scholarly communication | 0.025 | 0.030 |
| Open science | 0.004 | 0.027 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".