Social Networking and Academic Performance: A Generalized Structured Component Approach
Bibliographic record
Abstract
The proliferation of social networking sites (SNS) use by students has been accompanied by both concerns and excitement regarding the consequences of SNS use. Research on SNS use has become increasingly popular in the educational literature. There are a variety of ways that SNS use can affect students, and indeed the work in this stream of research has documented the links between SNS use and various outcome variables. One research question raised given the popularity of SNS with students—which has been both limited and inconsistent in published results—concerns the link between SNS use and academic performance. As SNS use increases, such questions aimed at disentangling the link have become increasingly important to address. However, related investigations have yielded conflicting results and are deficient in documenting the interplay and influences of other variables. The present study aims to clarify the association between SNS and academic performance by testing an exploratory model to examine the connections between SNS use, student-school traits, and academic performance. We suggest that educational researchers should distinguish between adaptive and maladaptive SNS use in academic settings.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".