Exploring the Impacts of TikTok on the Academic Performance of Chinese Secondary School Students
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
Since TikTok was released in 2016, more and more people have found TikTok interesting and have tried to become users. TikTok contains many features, such as video, chat, learning, and working. People can relax and have fun in their spare time with TikTok. Nevertheless, as TikTok has become increasingly popular, more students are becoming the primary users of TikTok. At the same time, the variety of short videos available on TikTok can lead to inconsistent content quality due to their low cost of production. As instructors, schoolteachers must know how students are affected when watching TikTok. After literature review, this paper mainly found the four areas of influence from TikTok that students will experience during the emergence phase: psychological influence, physical influence, behavioral influence, and positive influence. These four areas of influence indicate how instructors should properly guide students in using TikTok, which will provide references for future instructors and students in the education area.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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