Exploring the Effects of Social Media on Interpersonal Communication among Family Members
Bibliographic record
Abstract
Communication is essential toward all families and given the technology that we have today, Facebook has been one of many social media sites that lets people stay connected whereever they may be, although, not all members of the family are in to using Facebook to communicate with their loved ones. This study aims to determine the effects of social media on interpersonal communication among family members, in particular, it analyzes the effectiveness of Facebook and family communication. In connection with this, the emphasis of this study is the effects of social media on the quality of interpersonal communication skills among family members. A sample of 25% of 120 individuals from four different colleges during the 2016-17 school year were the respondents of this study. A questionnaire was given to the respondents which included their profile, number of hours and activities on Facebook, and lastly the quality of their interpersonal communications with their family members. The results of the study show that communicating through Facebook more than likely leads to misunderstandings among family members as the messages are not expressed properly. Hence, a family must take time to talk and interact with each other personally in order to avoid these kinds of conflicts and maintain a good family relationship.
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 | 0.014 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".