Atom Egoyan's Remember. Vices and virtues of the memory systems
Bibliographic record
Abstract
The extensive work of the Canadian-Armenian filmmaker Atom Egoyan has been characterized to addressing, among other topics, the ways in which we relate to the past and how our memory is mediated by various types of technology in the construction and conservation of our memories. In Remember (2015), his last feature film t to day, he returns to this topic aiming to represent the operation and the failures of memory. In the present essay, we will not carry out an aesthetic analysis but rather focus on the form and modes in which the memory is represented in this film. For this purpose, we propose to study it from an inquiry into the memory systems to account for its multiple layers and characteristics, just as we will account for some "sins" that will allow us to analyze not only its distortions but also its ways of working. Finally we will give space to think the place of the imagination in our relations with the past.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".