Gen Z and Digital Platforms: A Creative Process on Educating Gen Z on the Value of Crypto Currency
Bibliographic record
Abstract
The world of crypto currency is only recently starting to get popular and learning about its trajectory may help one make better financial choices and be more knowledgeable in the area of decentralization and digital advancements (Milutinovifá, 2018). This project aims to create a series of digital content that will educate Gen Z on the value of cryptocurrency using YouTube. I am looking to find the best practices for creating educational videos on crypto through online learning. Bitcoin and cryptocurrency are having an increase in user acceptance and use (Milutinoviƒá, 2018). Cryptocurrency's adaption will be an important topic to watch in the future because it can transform technology and alter how money is exchanged globally. Teaching it to the current generations is equally as important as finding out how to do it. Firstly, there must be an understanding about what kind of learners Gen Z are. Since they grew up in a generation of technology and are known as digital natives Generation Z find it easier to absorb information through an online format (Chunta, Shellenbarger, Chicca, 2021). It is also important to study the popular youtubers that create successful educational videos on the topic of cryptocurrency. Subsequently, what do the viewers say about the videos that increases views or what the viewer wants them to improve on. In this research there will come a conclusion on how to create template or find a creative flow or process in producing a series that will not only be educating but entertaining and easy to understand.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".