Bibliographic record
Abstract
Raul Ruiz famously referred to his 1985 adaptation of Robert Louis-Stevenson’s Treasure Island as a “trailer or user’s manual” for his entire cinema. Given that the film involved two years of pre-production, a three-month shoot, and then a tortuous six year post-production process in which both major financiers—Paulo Branco’s Les films du passage and 80s B-movie powerhouse Cannon Films—experienced bankruptcy, it would seem somewhat strange to the outsider, though not entirely uncharacteristic to the initiated that he would do so. This thesis addresses Ruiz’s self-assessment by doing two things: firstly, it attends to the relationship between theory and practice in the director’s cinema, using his writings as the foundation for a production history of the film (focusing, in particular, on Cannon’s attempts to legitimize and “civilize” its cinematic output, and the manner in which Ruiz’s emphasis upon the aleatory is reflected in the aesthetic scars of the film). Secondly, it uses this history—as well as the literary reading theory of Québec scholar Gilles Thérien—as a foundation for its central argument: Treasure Island is an allegory of the Ruizian reading process itself. Understanding the film in such a way opens the door for further academic work on the director’s adaptation process, an under-theorized area within the field of Ruiz study.
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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.058 | 0.015 |
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".