Directions in sound/image synthesis : study of bi-modality within emerging multimedia practice
Bibliographic record
Abstract
In recent years, due to the high level of technological standardization of the home computer, entertainment systems and high-speed web paraphernalia, our access to, desire for, and perspectives on various technological elements have been taken for granted. We have become acclimatized to the latest super-product promotion, web surfing, or interaction with the latest object-oriented programming software. Although audio often enhances graphical user interfaces (GUIs) to deliver feedback to users, interfaces in which sound is the principal element, or even shares a parallel domain, are atypical. Nonetheless, this practice is emerging as a viable junction within new technologies and digital media, encouraged by the ongoing development of mobile and all-pervading computing. Within emerging multimedia practice, spatial objects (visuals) require essentially to be structured in a two-dimensional space, whereas temporal objects (sound) require a sequential array. The goal of this dissertation is to develop a broader understanding of these two key elements, while providing designers and educators with useful information that is appropriate to familiar design discourse.* *This dissertation is a compound document (contains both a paper copy and a DVD as part of the dissertation). The DVD requires the following system requirements: Windows MediaPlayer or RealPlayer.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| 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".