Taking Advantage of Using Professionally-Oriented Social Network Sites: The Role of Users’ Actions and Profiles
Bibliographic record
Abstract
The main objective of this research is to propose a model that explains the process by which individuals develop social capital through using Professionally-Oriented Social Network Sites (P-SNSs) such as LinkedIn. The theoretical framework of the proposed research draws upon the extant literature in social network analysis and social capital theory. The proposed research model aims to explain how people’s investment on their social networks by building their profiles and actively participating on P-SNSs can lead to developing sources of social capital which, in turn, can provide them valuable benefits. Our research results show that profile disclosure and active participation positively affect perceived social connectedness. However, only profile disclosure positively affects network size, and there is no association between active participation and network size. This study differs from previous studies in this field in that it highlights the role of one’s profile in the social capital formation process on P-SNSs. \\ Keywords: \\ Social network sites, social capital, social network analysis, social connectedness, networking value, LinkedIn \\
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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 | 0.006 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".