Bibliographic record
Abstract
During the period of preemptive gossip leading up to the 2002 Academy Awards, a bustling debate erupted around Anthony Serkis’ digitally enhanced portrayal of the subhuman character, Gollum, in the film Lord of the Rings: The Two Towers. Serkis’ performance was digitally recorded using motion capture and CGI technologies that mapped his actions and facial movements (the actor’s voice was not adjusted or enhanced) into a software program to which digital graphic elements were added. The ensuing performance was sufficiently hybridized that Serkis’ status as live actor/referent seemed to fall into question, so much so that his inclusion as a possible nominee for Best Supporting Actor became a conundrum for the Academy. The debate circulated around whether Serkis’ performance could be considered “live” (regardless of the long history of analog and digital editing and “adjusting” in film acting production). Was Serkis present enough in the performance? At what point is something too digitized? If something is partially digitized, what of its ontology, its presence? Can someone (or something) perform, in the traditional sense, in the digital?
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".