Bibliographic record
Abstract
Electronic texts can be highly interactive. Forms of interactivity, such as hyperlinks, shortcuts or tags are not only signs to make meaning on the e-page but also space for actions and changing textual situations. Here, e-page interactivity and textual interaction become two different realms and make a gap between the text analysis and multimodal analysis. This case study aims to analyze the interactivity of the online tutorial booking and tracking application called ‘ ASP tutTrak’ by using a social semiotic multimodal framework for text and image analysis. The application is currently used in the Academic Success Center (ASC) of a tertiary level institution in the United Arab Emirates and was designed by the Information and Communication team of the same institution. The theoretical framework of the study presents multimodal social semiotics analysis of sites, signs and images of the ‘ASP tutTrak’ application through three metafunctions of communication (Halliday, 1978, Kress and Van Leeuwen, 1996, 2006). Since the application under consideration is in the academic domain, this study adapted Chou’s (2003) framework for interaction types for learner-interface and learner-content. Analysis of five sample pages indicate that the interactive meaning potentials of the digital text, interface and content are high and the application is interactive.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".