Internet Affects All Areas of The World: A Quantitative Report on Canadians Internet Usage
Bibliographic record
Abstract
The Internet has had a significant influence on modern life with Pew research reporting that 99% of American adults between the ages of 18-29 used the internet in 2021 with reported reduced internet use for older Americans. Both positive and negative consequences can be attributed to its widespread use. In attending Kennesaw State University internet use is almost mandatory to successfully complete coursework. Witnessing how active internet use is in collegiate age populations in the US is has sparked some curiosity of use in Canadian residents. The survey on "Canadian Internet Use", in the year 2018 was designed to measure the impact of digital technologies on Canadians' lives. The questions used were specifically designed to target internet usage, but many different demographics variables were also collected. As a result, this study will focus on Canadians’ internet usage as it relates to household income and age. This study will attempt to answer: What effect does household income and age have on reported internet usage in Canada? Expected findings for this study are a positive relationship between household income and the amount of internet used, and a negative relationship between , the age residents and internet usage. In addition, this study hope to confirm that internet usage is greater in 18-24 year old’s when compared to older ages. Overall, the information gathered will aid in better understanding of how Canadian residents use the Internet, including their frequency of use, demand for specific online activities, and online interactions. The resulting data will be used to inform evidence-based policymaking, research, and program development, as well as to provide international comparability in the use of digital technologies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".