Loop Structures in Film (and Literature): Experiments with Time Between the Poles of Classical and Complex Narration
Bibliographic record
Abstract
Among the many innovations complex or “puzzle” films have brought about in the last three decades, experiments with narrative time feature prominently. And within the category of nonlinear plots, the loop structure – exemplified by films such as Repeaters (Canada 2010), Source Code (USA/France 2011), Looper (USA/China 2012) or the TV-Series Day Break (USA 2006) – has established itself as an interesting variant defying certain norms of storytelling while at the same time conforming in most cases to the needs of genre and mass audience comprehension. In the first part of my paper, I will map out different kinds of repeated action plots, paying special attention to constraints and potentialities pertaining to this particular form. In the second part, I will address the issue of narrative complexity, showing that loop films cover a wide range from “excessively obvious” mainstream (e.g. Groundhog Day, USA 1992; 12:01, USA 1993; Edge of Tomorrow, USA/Canada 2014) to disturbing narrative experiments such as Los Cronocrimenes (Spain 2007) or Triangle (Great Britain/Australia 2009). Finally, a look at two early examples (Repeat Performance, USA 1947 and Twilight Zone: Judgement Day, USA 1959) will raise the question how singular the recent wave of loop films are from a historical perspective.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".