Bibliographic record
Abstract
Writers have invoked the concept of the “cinema of attractions,” from early cinema studies, to claim that horror films sacrifice narrative integrity to deliver sudden frights and spectacular shocks. An examination of the history of the concept of the attraction, however, finds it heavily theorized by Sergei Eisenstein as something that can bind films together in powerful ways. In one horror film, The Old Dark House (1932), slamming doors, quaking thunder, shattering glass and a rampaging mute butler, while scary, also figure in James Whale’s scheme to criss-cross his film with motifs and other repetitions and produce a work that gains with every viewing. Even with its thin narrative, stock characters and, already in 1932, very familiar story about characters trapped in an old dark house, the film hangs together in intricate ways. Most elaborately, Whale embeds attractions in a grid that overlays the tiered spaces of the setting. Characters move up and down the creaky staircases and along the suspended hallways, chasing each other, scuffling, and withholding and disclosing secrets. Scenographic and narrative space mesh into a tight unity lit up by a constellation of “fun house” jolts. Props, including lamps and knives, circulate through these spaces as well, tracing patterns that startle viewers while simultaneously rendering the film rigorously and beautifully coherent.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".