THE SOCIAL VALUES ATTACHED TO THE TIKTOK SOCIAL PLATFORM OF THE AGE CATEGORY 50+. A SOCIOLOGICAL PERSPECTIVE
Bibliographic record
Abstract
This paper analyzes the social values through which users over the age of 50 identify with the new TikTok social platform. Although the other online social networks are mainly aimed at a young audience, the TikTok application has overcome this barrier and included the age segment of over 50 years in the categories targeted by it. The ease with which one can make their proper creations and the intuitiveness of the application has made that two years after the launch of the new social platform a quarter of its users are in a more tangible age category of novelty and online interaction. The reality shows that a social application does not contradict people from early youth at all, and if you provide them with sufficiently clear tools to express themselves, they will take advantage of them and make their genuine creations. The present approach is based on existing data about the application in question and focuses on illustrating some values that emerge from the case study on TikTok.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".