Bibliographic record
Abstract
This research paper brings together the fields of systems engineering and media studies to investigate the cinema/television/computer/mobile device screen as a dynamic interface through which points of engagement or how the aesthetics and narrative structures presented on the screen engage the user and create meaning. The co-authors work towards the development of a “screen real estate grammar” or ontology by considering the following set of questions: 1. How can the specific structures (ie/ uses of time, space, text, screen resolution, window size, etc.) of user interfaces (ie/ iTunes and QuickTime X Windows) be mapped? 2. Will such mapping expose levels of convergence (ie/ where old forms meet/influence/contribute to new developments and new content? 3. Is it possible to work towards a language of conventions similar to that of other disciplines? Ie/ film language 4. Can interface elements be prioritized on a contextual basis? The framework is presented in the context of a decision support system for user interface optimization, which allows interfaces to be dynamically adapted to different formats given a set of rules that create a semantic mapping between interface elements. Generative programming is then used to create the optimized interface.
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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".