Lost in Déjà Vu—The Textual Analysis of Nettles Based on Intertextuality
Bibliographic record
Abstract
In this thesis, the author focuses on the application of intertextuality depending on the textual analysis of Nettles in order to demonstrate how the art of intertextuality helps to achieve this story by employing other texts to serve this story. Just like a mosaic, this text is a coinage of quotations and a combination of absorption and transformation of anther texts. As a result, this “mosaic” creates Déjà Vu —a kind of phenomenon that is somewhat similar to what recently happened, or something reminded you of something that happened in the past which you seem to have already encountered before. In this thesis, gender differences, gender roles, usage of different sources and readers’ former images and knowledge will be involved in the analysis of this story from the angle of intertextuality. This thesis will help to enlighten how intertextuality being a device to create Déjà Vu phenomenon, and in what world that can people get lost.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".