Bibliographic record
Abstract
Adolescents spend a significant and increasing amount of time online, especially on social media (SM) platforms. Replicating most of the questions from the 2018 PEW Teen Survey (Anderson & Jiang, 2018), this study provides a current understanding of how a sample of Canadian adolescents use SM, what perceived effects of SM they report, and whether there are noticeable differences in some of the responses, broken down by demographic variables. Results indicate that Canadian teens are plugged into SM platforms on a near constant basis and that Snapchat was the most popular platform used followed closely by TikTok. Connecting with friends was most often reported as a positive effect while feeling pressure to post content that makes them look good as well as experiencing unrealistic views of others’ lives, were most often reported as negative effects. There was no statistically significant difference in the overall effects of SM use in relation to the demographic subgroups of age, gender, ethnicity, and socioeconomic status. However, there were notable variances for other research variables such as posting behaviors and playing video games related to gender, whereby girls posted more selfies and felt more pressure to post content for likes while boys played more video games. Both the American and Canadian studies indicate declining popularity for the platform Facebook, with even fewer Canadian teens using it. Additionally, there are similarities in the posting behaviors of both teen populations. This study also reports on the effects of the Covid pandemic in relation to the frequency of SM use and future outlook. Canadian teens overwhelmingly reported an increase in SM use due to the pandemic and an overall pessimistic outlook for the future. Implications of this study include more strategic education and government initiatives based on the most current understanding of teen social media use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".