Students With and Without Disabilities Using Social Media: Relationship Benefits and Implications for Education
Bibliographic record
Abstract
A pandemic in 2020 resulted in economic and social disruption of unprecedented scale. Social distancing — or physical distancing while in public spaces — was required, and social media usage spiked globally as people turned to these online spaces for information and connection. Today’s postsecondary students, in particular, are frequently immersed in social media; it can offer them social supports, such as a greater sense of belonging during times of transition and crisis, but also inherent risks, including cyberbullying and online harassment. Although many studies have examined the social connections or supports for learning that college students without disabilities experience by using social media, few studies have explored these phenomena among college students with disabilities, including neurodevelopmental disabilities such as anxiety disorders (e.g., social anxiety, autism, attention deficit disorder) that make socialization difficult for these young adults. It is important that educational research advances understanding of the socialization experiences of these students with disabilities because students’ sense of belonging and peer support is critical to their engagement and success in K-12 and postsecondary schooling.
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 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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".