Casual Authoring using a Video Navigation History
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
We propose the use of a personal video navigation history, which records a user’s viewing behaviour, as a basis for casual video editing and sharing. Our novel interaction supports users’ navigation of previously-viewed intervals to construct new videos via simple playlists. The intervals in the history can be individually previewed and searched, filtered to identify frequently-viewed sections, and added to a playlist from which they can be refined and re-ordered to create new videos. Interval selection and playlist creation using a history-based interaction is compared to a more conventional filmstrip-based technique. Using our novel interaction participants took at most two-thirds the time taken by the conventional method, and we found users gravitated towards using a history-based mechanism to find previously-viewed intervals compared to a state-ofthe-art video interval selection method. Our study concludes that users are comfortable using a video history, and are happy to re-watch interesting parts of video to utilize the history’s advantages in an authoring context.
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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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