Bibliographic record
Abstract
During covid 19, traditional offline social interaction becomes difficult. Benefiting from the availability of social media, people are increasingly relying on social media to socialize. Can social media interaction replace real-life interaction? Is a social media-based relationship a sufficient substitution for a real relationship? This article divides social media interaction into reciprocal and non-reciprocal and compares two different social media interactions with real-life interactions. For the reciprocal social media interaction, The first research question distinguishes computer-mediated communication (CMC) from face-to-face (FtF) and discusses the possibility of CMC replacing FtF. Because the absence of social cues cannot be made up, CMC is not a sufficient substitute for FtF. For the non-reciprocal social media interaction, the second research question focuses on parasocial interaction-induced parasocial relationships. Because of non-reciprocality and lack of authenticity, the parasocial relationship is not a good substitute for a real relationship. In conclusion, social media relationship is not a sufficient substitute for a real relationship, but they can be used as a good supplement to a real relationship. The difference between reciprocal social media relationships and non-reciprocal social media relationships is also discussed. Reciprocal social media relationships and real relationships are interchangeable, while non-reciprocal social media relationships cannot transform into real relationships.
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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".