“A Copy of a Copy of a Copy”: Productive Repetition in <i>Fight Club</i>
Bibliographic record
Abstract
There are two types of repetition: one that repeats based on identities and one that repeats differences. While the former is the common-sense view that understands difference to be the difference between two substantial things, the latter argues that, against this common-sense interpretation of repetition, it is difference itself that repeats and that, in fact, it is a failure to repeat identically that defines this latter version. Through an analysis of Deleuze’s conception of repetition as a repetition of difference in itself, this paper interprets Fight Club as a vehicle for addressing the question of how it is possible for something new to arise out of a seemingly stifling world of repetition and concretized identities. Given Deleuze’s ontology of immanence, fictional characters are no less real than flesh-and-blood people and, therefore, it is argued that alternative readings are actually offered up by the text itself, and not imposed by the reader from a transcendent point of view. In other words, literary texts are opened up to their own immanent becoming, a becoming that eventuates not only in the altering of the terms of the text, but, given the plane of immanence, in the reader as well.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.026 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".