The Legislations and Regulations of Social Media- A study of 2 countries – United States, Canada
Bibliographic record
Abstract
In today’s society, social media is widely popular to use for many different reasons such as communicating, marketing, networking etc. However, with social media rapidly expanding and the usage growing exponentially, it can become dangerous. Some of these dangers include privacy exposure, invasion of privacy, cyberbullying, harassment, exploitation etc. One of the bigger challenges faced is protecting children and minors on social media, and preventing false news and rumors from circling. While some countries have stricter social media laws and rules implemented by the government, other countries rely on the social media platforms themselves to regulate and protect users from the danger of social media. In this research, two countries; Canada and the United States of America, will be analyzed comparatively based on social media freedom, laws and regulations implemented on the use for social media and what they are for, as well as how they can protect users. The purpose of this research is to find out the political, social and legal system of the two countries under study, to find out the social media use of the countries, to find out the laws, regulations and legislations that are being enacted and implemented on social media in the two countries under study, to find why the legislations were put in place and to find out the reactions from citizens in respective countries regarding these laws.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".