Cyberbullying, Social Media & Fitness Selfies: An Evolutionary Perspective
Bibliographic record
Abstract
The general goal of the current research was to explore how social media influences a variety of aspects of young adults’ lives, including motivation to be physically fit, and bullying behaviors. The specific objectives were to investigate the link amongst selfie, social media use, and cyberbullying in relation to physical fitness through the lens of evolutionary psychology. Brock University students (N = 83, 73.5% female) between the ages of 17 and 25 were recruited who have had some level of experience with fitness or living an active lifestyle. Participants completed self-report measures based on bullying/victimization experiences, cyberbullying, personality, narcissism, self-esteem, selfie use, physical activity, and self-body image. Based on evolutionary principles, it was hypothesized that those who post selfies are more likely to have been previously victimized. It was also hypothesized that males would have a stronger drive towards being physically fit, females would be more likely to be positively motivated to work out after viewing fitness selfies, and males would be more likely to view their peers as competitors and to have higher levels of jealousy. The results suggest that females were more likely to be motivated when viewing these fitness selfies, but also were more likely to be jealous of the types of body shapes posted. There was little effect on males in regards to viewing fitness selfies, suggesting that females are overall more engaged and influenced by this type of social media. The overall implications of the study suggest that technology and social media do encompass positive and beneficial qualities. Furthermore, social media should be engaged judiciously to educate young people about its positive \nuse as well as inform them about the possible negative impacts of the digital world.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".