Bibliographic record
Abstract
Particularly to the younger Millennials and Generation Z, it appears as if social networking sites (SNS) is coming to the point of replacing normal social interactions and removing much of the personal aspects out of socialization. With the Internet literally being able to move at the speed of light, face-to-face interactions appear to be slowly in decline. As a highly social species, humans require interaction to maintain a healthy psychological state. This research has been conducted to analyze the level of loneliness and the level of SNS use with the intent of reinvestigating previous research on the correlations of SNS and loneliness with a more diverse demographic sample. In addition, this study has been designed to see if high usage of specific platforms has an increased likelihood to be related with loneliness. This research has been conducted by means of an anonymous survey of college students at a university in the Pacific to determine the amount of time spent on SNS, activities conducted while utilizing SNS, the priorities placed, and other information in regards to SNS usage. Inquiry was also conducted in analyzing how interpersonal relationships are related to or affected by SNS. This has been combined with the revised UCLA loneliness scale to determine if there is a correlation between SNS use, specific platform use, and loneliness. While previous similar studies have been conducted, the two primary differences are the diversity in the demographics available to be surveyed and the attempt at identifying if a specific platform is more likely to be related with loneliness.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".