Positive developmental video classification for children
Bibliographic record
Abstract
This paper introduces the concept of positive developmental video classification. The work focuses on developing features and classification systems that can be used to classify content based on the impact on the cognitive, social and academic development of children according to an expertly assigned predefined positive or negative cognitive impact category. We solve the problem by developing novel features that gauge the amount of social interaction, attention disrupting fast-paced content, incorporate music information retrieval features and combine these features with other video content analysis features. This information is then used to determine what content has a positive impact on a child's development. It was found that the low-level features can be used for classification and do have correlation with expertly assigned predefined impact categories. To ensure the validation results are not based on similarities between content, a new model validation technique is developed to ensure that the videos are classified with respect to their impact on development. In addition, we developed a data set of videos that has been classified as having a positive or negative impact on children, based on expert experimental results in the psychological literature. This data set can be used as a benchmark for future research. Validation results found the system had almost 30% better accuracy than state-of-the-art video genre classification systems and over 65% better performance than the arousal time curve used in affective video content modelling.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".