Student Independent Projects Psychology 2015: Are Cell Phones Making us Asocial? \nRelating Cellphone Usage to Asocial Behavior
Bibliographic record
Abstract
There is a variety of research done on cellphone usage in general along with social \nnetworking sites (SNS) like Facebook, Twitter, Instagram, and Snapchat with specific regard to \nthe consequences of what influence it can have on a user. Now that technology allows us to use \nthe internet in the palm of our hand, is technology changing personalities? Is it changing the way \nwe communicate and socialize with others? The question that I answer with this paper is whether \ncellphones and SNS are making us social, like the name suggests, or is it actually having the \nopposite effect and making us asocial? To this end, I am going to examine different categories of \ncellphone usage and relate them back the characteristics of being asocial. The measure that I will \nbe examining are cellphone, internet, and social networking addictions, attitudes towards work \nand school when using cellphones, relationships and cellphone use, internet use and depression, \nas well as cyber bullying and cellphone usage. With cellphones making communication very \naccessible, where you do not have to step outside the door to have a conversation with someone \nor to watch a movie because you can do it in the palm of your hand, I think cellphones may in \nfact be leading people into an asocial lifestyle.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.173 | 0.045 |
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".