Bibliographic record
Abstract
Cyberspace has become a common place for youth to involve in online networking and to make new friends. This paper focuses on: a) the dynamics of cyberfriendship formation; b) the role of self-disclosure, alienation, and frequency of online interaction on formation of cyberfriendship; and c) the extent of satisfaction achieved from that relationship. A stratified random sample of 250 youth (Mean age = 21.16, SD = 1.36) studying in different undergraduate faculties from a large Malaysian university responded to self-tailored questionnaires. The results revealed that 85.6% youth have formed online friendship using multiple communication channels. Major findings of this research indicated that the combined use of communication channels (i.e., Instant Messengers, e-mail, and Social Networking Sites), together with preexisting face-to-face friendship and loneliness, mediated through online self-disclosure and frequency of online interaction contributed to the formation of cyberfriendship. In addition, loneliness was identified as a contributing factor for cyberfriendship formation among youth. Discussion includes implications of the present findings and suggestions for future research.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".