Bibliographic record
Abstract
In the world of the printed page, pictures and graphic layout are generally taken to be mere complements or exemplifications of the verbal part of the text. This is not only against the principles of semiotics, it is against the very rules of communication, as the text is not perceived by the reader as a sum of different dimensions (i.e., verbal, visual, tactile, etc.) but as a whole where all components are connected and interdependent. Thus, splitting a text into its several dimensions is a completely artificial procedure that should be carried out for analytical purposes only, since all components of a text, as well as their interplay and the interplay of the text with its context and co-text, contribute to the construction of meaning. This cannot be ignored by translators and should be made clear to any client who may think that the act of translation is by definition limited to the verbal dimension, and may go as far as submitting texts for translation without providing briefs about the visual elements they will be “complemented” with, or the graphic conventions that will be adopted in the final version. The translation – or localization – of advertisements is a case in point. The visual component plays a prominent role in most forms of advertising, particularly so in magazine ads; developing the pictorial and graphic aspects of a campaign, however these might appear “casual,” is a time-consuming and expensive process. If the translator (or localizer) is responsible for the text resulting from his/her work, then, he/she cannot ignore its visual dimension, and should be prepared to suggest modifications not only to the verbal part of the text, but also to its many other dimensions, in a holistic, intersemiotic perspective. Real-life examples, not only from advertising but also from editorial translation, will be provided to support this argument.
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 | 0.023 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.066 |
| Scholarly communication | 0.021 | 0.021 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".