Life online during the pandemic : How university students feel about abrupt mediatization
Bibliographic record
Abstract
Abstract The COVID-19 pandemic caused university education to transition from face-to-face contacts to virtual learning environments. Young adults were forced to live an entirely new life online, without valuable and enjoyable social interaction. We examined subjective perspectives towards life online during the pandemic. We identified four viewpoints about life mediated by computers. Two viewpoints express “struggling”: Viewpoint 1 (Angry, Depressed and Overwhelmed), and Viewpoint 3 (Restricted to and Overwhelmed by Virtuality). A third feeling-state conveys experiences of “surviving”: Viewpoint 4 (Isolated and Powerless in Convenience). Surprisingly, Viewpoint 2 is about “thriving” (Comfortable and Convenient Routine with Computers). The research shows that virtualization, confinement, and anxiety are taking a toll on the mental health of some members of the younger generation, while at the same time other members feel they are thriving in a situation of limited resources, virtuality, and reduced face-to-face human interaction.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".