Análisis Bibliométrico de la Animación 3D en el Contexto de la Cultura Digital
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 study presents a comprehensive bibliometric analysis of 3D animation research within digital culture from 2000 to 2024. The findings show steady growth in scientific production up to 2021, driven by rapid technological advances and their application in various fields such as education, medicine, and entertainment. China, the United States, and Canada stand out as the main contributors, although China’s lower level of international collaboration points to opportunities for strengthening global research networks. The keyword analysis reveals a marked increase in topics related to virtual reality, augmented reality, and e-learning, reflecting the adoption of immersive technologies in both educational and professional contexts. Likewise, emerging areas centered on artificial intelligence and machine learning broaden the spectrum of applications and improve efficiency in 3D animation production. Limitations include the reliance on a single database and the exclusion of non-English documents, which could underestimate significant contributions in other languages.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it