Interpretive Strategies for Screen-Based Creative Technologies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper brings together the disciplines of media and creative technologies studies and software systems engineering; it focuses on the challenge of finding methodologies to measure, test and decode meaning in digital cultural objects. Just as rough set theory is a mathematical tool to deal with vagueness and uncertainty in artificial intelligence, and approximation accuracy and knowledge granularity are approaches to uncertainty research, the authors argue that découpage analytique is a possible method for decoding screen-based information. They draw on a variety of examples: interactive online digital art projects; an interactive, immersive screen-based art installation; re-mediated digital art installation; expanded cinema; a videogame; and a medical interface example, in order to determine if it is possible to map interpretive strategies that include a blending of old and new criteria, but ultimately promoting an equal partnership between artist and audience, and thus, a community of co-creators. Additionally, the authors present experimental evidence on the difference introduced by the screen size to further qualify the effectiveness of découpage analytique in relation to the amount of screen real estate afforded.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it