Should it Stay or Should it Go? Smartphone Dependency
Bibliographic record
Abstract
As smartphones grow in use and popularity, it is important to understand the possible effects that varying levels of smartphone use may have on human cognition. Although smartphones provide many advantages for daily activities, one must also recognize the potential disadvantages. For example, smartphone use may lead to nomophobia, which is defined as the modern fear of not being able to access your smartphone or the internet (Yildirim & Correia, 2015). The present study used a pilot and main study to examine the effects smartphones have on human cognition. The pilot study was conducted to measure nomophobia, mobile phone involvement, smartphone attachment and dependency, and general smartphone use. This portion was also used to determine the paradigm for the main study. Participants in the main study completed the 12 Cambridge Brain Science tasks, which measured different aspects of cognition' while leaving their smartphones in one of two locations: on their desk, or outside of the testing room. Additionally, participants completed the same four questionnaires from the pilot study. Results from both studies reveal the majority of individuals show moderate levels of nomophobia, dependency and attachment, and involvement. Subsequent data analysis focused on the double-trouble task, which is an attention-based task. Results found that there was no significant difference in performance on the double-trouble task between the two locations. Contrary to common belief, it seems that the mere presence of one’s smartphone does not affect performance on a cognitively demanding task.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".