"I can’t! I can’t! I can’t!" : un cri, un coup de feu "a shout and a shot"
Bibliographic record
Abstract
This paper will try to show how the book purports to be digging for the past, aiming at uncovering and recovering trauma, both collective and individual, thanks to recapturing the past for a character, for a reader, for a nation. The Wars, a novel by Timothy Findley, tries to make sense for the narrator but also for the reader of a founding image which opens the book and of other allegedly material images, photographs, interviews, archives. Robert Ross, a Canadian soldier who held a controversial role in the first World War, the war to end all wars, stands at the centre of this memorial reconstruction. Photographs, as described in the text, give rise to ekphraseis which often appeal to the reader’s sensitivity thanks to a detail which might correspond to what Roland Barthes described as the punctum. That is the sharp point which shoots from the photograph and pierces the ordinariness of the gaze, the studium. It leaves the horizontal plane of the image and rises up. It is the point of exchange between reception and emission and it introduces confusion in the spectator’s place. This is when image comes under view and when the traumatic symptom pierces the calm of everyday life with the eruption of the violence of an inhuman collective and personal past.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".