Bibliographic record
Abstract
This project analyses the representation of accidents in film and television. Much of the literature that has studied the accident (and the relationship of accidents to media) has done so under the auspices of the accident’s relationship to modern technology (that is, analysing how the accident relates to the unforeseen failure of technology). This project, in contrast, seeks to explore a different facet of the accident: to examine how the representation of accidents can give us new understandings of the phenomenological conditions that give rise to events we interpret as accidents, and how mediation can provide us with ideas about the phenomenological structure of the accident. The first half of the dissertation examines the representation of accident causality. Some of cinema’s mechanisms, such as the use of cross-cutting to show simultaneous action in multiple locations, allows spectators to gain a new perspective on causality, one that would be impossible in a first-hand experience of the accident, and thus opens a new opportunity for reconsidering or differently framing the ontology (and etiology) of the accident. This can help us better understand the phenomenological conditions that account for accidents, and how individuals relate to the predictable elements of their world as well as those things that arrive unforeseen. The second half of the project looks at the effects that accident representations might be said to have on the spectator. Starting with the concept of tragedy, I look at how emotional response is tied to an understanding of the fragility of human being in our world: that we are vulnerable to circumstances that play with knowledge and ignorance to devastating effects. This opens into a broader discussion of the use of accident representations, and the different ways that images of accidents might be taken up and used existentially or philosophically. Encountering representations of accidents opens the possibility of changing ones thinking about these kinds of events, how they work, how they can affect our world, and thus serves as a fertile ground for reimaging these events and our understanding of being-in-the-world.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.010 | 0.020 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".