Bibliographic record
Abstract
Though Jakobson conceptualized inter-semiotic transfer as a valid form of translation between texts some 40 years ago, it remained a relatively marginal area of investigation until recently. Additionally, we may note that information and communication technologies (ICTs) have also markedly modified previously held notions of what constitutes a “text”. Basing my research on these two observations, this paper will draw attention to inter-semiotic translation within the space the newscast, a prime example of an ever-evolving multi-modal text created by newer forms of ICTs. More specifically, the focus lies in how we can posit translational activity in the construction of the newscast (for instance, how the “visual” images translate the “verbal” narration of the newscaster or journalist). This conceptualization of “news-making” will lead us to consider the ways in which cultural stereotypes are created through the “translation” of interacting texts (verbal, visual, aural) on the same interface. To suggest that newscasts, and by extension media, proliferate cultural stereotypes, is by no means novel. However, to consider how inter-semiotic translation plays a role in their creation may be a departure from previous paradigms. In fact, Kress and van Leeuwan state: “This incessant process of ‘translation’, or ‘transcoding’ – ‘transduction’ – between a range of semiotic modes represents […] a better, more adequate understanding of representation and communication (2006:39)”. Furthermore, because cultural stereotyping is often at the root of conflict, this type of investigation becomes, we suggest, all the more worthwhile in understanding how we “translate” difference across borders, semiotic or otherwise.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.028 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".