Bibliographic record
Abstract
After the many interdisciplinary perspectives on nonverbal communication offered by the author in his previous seven John Benjamins books, which have generated a wide range of scholarly applications, the present monograph is dominated by a very broad concept of translation. This treatment of translation includes theater and cinema (enriching our intellectual-sensorial experience of both 'reading act' and 'viewing act') and offers among other topics: sensorial-intellectual-emotional pre- and post-reading interactions with books; mute or audible 'oralization' of texts; the translator's linguistic and nonverbal-cultural fluency and implicit textual paralanguage and kinesics; translating functions of pictorial illustrations; the blind's text and film perception; the foreign reader's cultural background and circumstances; theater and cinema spectators' total sensory-intellectual experience of plays and films beyond staging or projection; the multiple interrelationships between cinema and theater performers, spectators and their environments, of special interest to all those involved in the theater; and the translator's challenging textual perception of sounds and movements. Over 800 literary quotations, and two virtually exhaustive English inventories of sound- and movement-denoting words with many examples, offer serious students of translation, language or literature a rich reference and drill source.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.053 | 0.017 |
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".