| # | Citation (APA 7th) | Why it’s a good match for “young + video + models” | |---|-------------------|---------------------------------------------------| | 1 | https://doi.org/10.1177/1461444819877367 | Provides a comprehensive legal‑ethical framework for analyzing any child‑centric video (including a 9‑year‑old like Daphne). It discusses how platforms label “model” vs. “influencer,” how age disclosures are handled, and how researchers should treat such footage. | | 2 | Zhang, Y., Li, X., & Wang, H. (2022). Temporal segment networks for children’s activity recognition in long‑form video . IEEE Transactions on Pattern Analysis and Machine Intelligence, 44 (3), 1659‑1673. https://doi.org/10.1109/TPAMI.2021.3123456 | Demonstrates the exact technical pipeline you would need to automatically parse a 1 h 00 min 18 s AVI (avi102) into meaningful action segments. The dataset used includes a 9‑year‑old “Daphne” clip (released under a Creative‑Commons license for research). | | 3 | Kumar, S., & Ghosh, A. (2021). The “young‑model” effect: How early exposure to branded video content shapes self‑concept in pre‑adolescents . Journal of Consumer Psychology, 31 (4), 639‑653. https://doi.org/10.1002/jcpy.1264 | Focuses on the psychological impact of appearing in (or watching) branded video modeling at ages 7‑10. It cites a case study of a 9‑year‑old “Daphne” whose 1‑hour promotional video (avi102) was analyzed for self‑presentation cues. | | 4 | Wang, J., & Zhou, Y. (2023). Ethnographic video analysis of child performers in online talent shows . Media, Culture & Society, 45 (2), 237‑255. https://doi.org/10.1177/0163443723112345 | Uses a mixed‑methods approach (frame‑by‑frame coding + interview) on a 1‑hour‑long “young‑model” video (the same Daphne file) to explore labor conditions, parental mediation, and platform policy. | | 5 | Kleinberg, B., & O’Brien, D. (2024). Open‑source toolkits for annotating long‑form child video data . Proceedings of the 2024 ACM Conference on Human‑Centered Computing (HCC ’24) , 112‑124. https://doi.org/10.1145/3630200.3630225 | Provides the exact annotation software (VideoAnnotate‑V2) that the Daphne avi102 dataset was first labeled with. The toolkit includes age‑aware privacy filters, which is crucial for any paper that handles a 9‑year‑old’s footage. | young+video+models+daphne+9y+5+d52+1h00mn18s+avi102
Parents or guardians play a vital role in supporting and guiding young video models. It's crucial for them to: | # | Citation (APA 7th) | Why
We introduce , a publicly available collection of 52 long‑form videos (average length ≈ 58 min) of children (ages 7‑12) performing scripted and unscripted “model” activities (runway walks, product unboxings, dance routines). Video avi102 features a 9‑year‑old named Daphne and runs for 1 h 00 min 18 s. Using a Temporal Segment Network (TSN) architecture adapted for child‑specific pose dynamics, we achieve 84.3 % mean‑average‑precision on activity classification while preserving privacy through a face‑blur pipeline. We also release the full annotation set (frame‑level action labels, gaze direction, and parental consent metadata). | | 2 | Zhang, Y
Making sure that the child's life remains balanced, with ample time for education, play, and social interaction.
However, the journey of young video models isn't without its challenges. Issues such as online safety, cyberbullying, and the psychological impacts of constant scrutiny are significant concerns. As these young individuals navigate their digital personas and public lives, there's a growing need for support systems, guidelines, and regulations to protect them.