Bibliographic record
Abstract
Many child-related issues receive ample attention and forms of action; however, the issue of kidfluencers has mainly gone unnoticed despite it occurring in front of our eyes. 'Kidfluencer' is a term that describes a child who is on social media platforms with a significant following and who is posted in video content (Suleiman, 2022). This paper will focus primarily on kidfluencers who post content on the social media platform called YouTube but will also include mention of TikTok and Instagram. Two kinds of kidfluencer channels will be examined: non-vlogging channels that focus primarily on the kidfluencers with limited to no parental figure interaction and family vlogging channels where there is some parental involvement but most attention is on the kidfluencers. The reason that kidfluencers are a cause for concern is that there are extensive ethical issues, worries of sexual exploitation, adverse psychological effects, problems associated with monetization and regulation and the forfeiture of a typical childhood. Overall, kidfluencers are a highly vulnerable group and are primarily unprotected from various types of exploitation.
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.
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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".