Review of Interactive Video–Romanian Project Proposal
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
<p class="apa">In the recent years, the globalization and massification of video education offer involved more and more eLearning scenarios within universities. This article refers to interactive video and proposes an overview of it. We analyze the background information, regarding the eLearning campus used in virtual universities around the world, the MOOC movement in the last year, and the related interactive video platforms in the (education) field. At the same time, we pay particular attention to technical aspects of the interactive video: defining concept, types of video metadata, media fragments and types of annotations, as primordial elements that bring interactivity. We tested some free and commercial interactive web application. We gathered all the ideas. We propose a framework for an interactive system web based on the main modules: video resource management (production, transcoding and storage), annotations, Linked Open Data, distribution medium, player interface, data analytics and recommendation system. On the way, we offer our findings, together with our recommendations for an annotation interface and player module. It is our idea for Politehnica University Timisoara, either as a standalone solution or a complement to actual virtual campus (http://cv.upt.ro) depending on future development plans and financial aspects.</p>
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 | 0.005 |
| 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) | 0.000 | 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