Editorial: Digital Genres and Multimodality
Bibliographic record
Abstract
Today's online science production and dissemination practices are far from being monolingual, monomodal or scarce in available genres (cf., e.g., Luzn & Prez-Llantada, 2022, or the review in this issue).Quite on the contrary, the digitalization (or digitization) of science communication (Bucher, 2020;Knneker & Lugger, 2013) has brought about unprecedented access to a profusion of diverse semiotic resources which researchers need to harness for the effective diffusion and promotion of their investigative activities (Luzn, 2019;Xia & Hafner, 2021).Facing this complex panorama, the field of English for Specific Purposes (ESP) plays a crucial role in its endeavour to analyse and systematize the pedagogy of specialized discourse use in relation to the communicative practices of professional communities.At this juncture, this Special Issue of ESP Today puts together a collection of state-of-the-art research touching on web-mediated emerging genres from the perspective of ESP genre studies, complemented by two instances of research into digital instructional genres (for a justification of this decision see e.g., Bondi, 2016).More specifically, the issue intends to combine aspects of rhetorical and contextual analysis together with studies investigating the discourse features of these genres as expressed in their use of digital multimodal semiotic resources.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".