"Suit the Action to the Word, the Word to the Action": An Unconventional Approach to Describing Shakespeare's Hamlet
Bibliographic record
Abstract
[First paragraph]: "Without access to audio description, individuals who are visually impaired (that is, are blind or have low vision) may be at a unique social disadvantage because they are unable to participate fully in a culture that is based on and heavily saturated by the enjoyment of audiovisual entertainments (Packer & Kirchner, 1997). Audio description was introduced as an adaptive "after-the-fact" strategy to give individuals who are visually impaired better access to entertainment media (Fels, Udo, Ting, Diamond, & Diamond, 2006). With audio description, visually important elements of the "entertainment experience" are described during pauses in the dialogue (Packer & Kirchner, 1997). Conventional audio description practices, as outlined by Snyder (2005, 2007), have been adopted as an access strategy for live theater, television, and film, although little research has informed these practices (Gerber, 2007). Alternative audio description strategies are also being explored and developed, mainly by theater (for example, British Council for the Arts, 2007; Graeae Theatre Company, n.d.) or dance troupes (for example, CandoCo Dance Company, 2008; StopGap, 2008) whose mandates are focused on the inclusion of individuals with disabilities in their casts."
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 | 0.002 | 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 | 0.008 | 0.013 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".